EP4723954A2

EP4723954A2 - Pediatric and adult congenital cardiac phenotype prediction using electrocardiogram

Info

Publication number: EP4723954A2
Application number: EP24820033.9A
Authority: EP
Inventors: Joshua MAYOURIAN; John TRIEDMAN
Original assignee: Boston Childrens Hospital
Current assignee: Boston Childrens Hospital
Priority date: 2023-06-07
Filing date: 2024-06-06
Publication date: 2026-04-15
Also published as: WO2024254299A3; WO2024254299A2

Abstract

Disclosed herein are techniques for evaluating cardiac health of a pediatric or adult congenital patient, including techniques for generating one or more models (e.g., machine learning models) trained to predict a cardiac condition in a pediatric or adult congenital patient. Some methods may include receiving electrocardiogram (ECG) data of a pediatric or adult congenital patient, determining whether the patient corresponds to one or more of multiple cardiac phenotypes, each corresponding to a presence of a cardiac condition based on analysis by one or more trained models, and outputting the cardiac phenotype(s) based on the ECG data. In some embodiments, the cardiac condition may include one or more of mortality, ventricular dysfunction, ventricular dilation, and ventricular hypertrophy. In embodiments that use one or more trained models, the trained model(s) may be trained using training data that includes ECG data from multiple prior pediatric or adult congenital patients and information indicating whether each prior patient had one or more cardiac conditions.

Description

PEDIATRIC AND ADULT CONGENITAL CARDIAC PHENOTYPE PREDICTION

USING ELECTROCARDIOGRAM

CROSS-REFERENCE

[0001] This application claims benefit to U.S. Provisional Patent Application Serial No. 63/471,718 filed on June 7, 2023, U.S. Provisional Patent Application Serial No. 63/603,403 filed on November 28, 2023, and U.S. Provisional Patent Application Serial No. 63/575,540 filed on April 5, 2024, which are incorporated herein by reference in their entirety for all purposes.

BACKGROUND

[0002] Pediatric and adult congenital patients, both healthy and those experiencing cardiac difficulties, may need to be evaluated for whether their symptoms demonstrate the presence of a particular cardiac-related medical condition. Patients have generally relied upon MRI or echocardiograms for cardiac screening, which have high reliability and have been in wide use.

SUMMARY

[0003] In some aspects, the techniques described herein relate to a method of predicting a cardiac condition in a pediatric or adult congenital patient, the method including: determining, based on received electrocardiogram (ECG) data of a pediatric or adult congenital patient, one or more of a plurality of cardiac phenotypes to which to assign the pediatric or adult congenital patient, at least one cardiac phenotype of the plurality of cardiac phenotypes each corresponding to presence of a cardiac condition, the determining the one or more of the plurality of cardiac phenotypes to assign to the pediatric or adult congenital patient including analyzing the received ECG data using one or more trained models, wherein the one or more trained models were trained with training data including ECG data from a plurality of prior pediatric or adult congenital patients and information indicating whether each prior patient of the plurality of prior pediatric or adult congenital patients had one or more cardiac conditions; and outputting the one or more of the plurality of cardiac phenotypes determined for the pediatric or adult congenital patient based on the ECG data.

[0004] In some aspects, determining the one or more of a plurality of cardiac phenotypes to which to assign the pediatric or adult congenital patient includes determining a probability of the pediatric or adult congenital patient having one or more cardiac conditions.

[0005] In some aspects, the techniques described herein relate to a method, wherein: determining a probability of the pediatric or adult congenital patient having one or more cardiac conditions includes determining a probability of the pediatric or adult congenital patient having any of the one or more cardiac conditions; and determining the one or more of the plurality of cardiac phenotypes to which to assign the pediatric or adult congenital patient includes, based on the probability of the pediatric or adult congenital patient having any of the one or more cardiac conditions, determining whether to assign the pediatric or adult congenital patient to a phenotype for pediatric or adult congenital patients with any of the one or more cardiac conditions.

[0006] In some aspects, determining a probability of the pediatric or adult congenital patient having one or more cardiac conditions includes determining a probability of the pediatric or adult congenital patient having each of the one or more cardiac conditions, wherein the one or more cardiac conditions include mortality, ventricular dysfunction, ventricular dilation, and ventricular hypertrophy.

[0007] In some aspects, determining the one or more of the plurality of cardiac phenotypes includes comparing the probability of the pediatric or adult congenital patient having each of the one or more cardiac conditions to a threshold.

[0008] In some aspects, determining one or more of the plurality of cardiac phenotypes includes determining one or more of a mortality risk, ventricular dysfunction phenotype, ventricular dilation phenotype, and ventricular hypertrophy phenotype.

[0009] In some aspects, determining one or more of the plurality of cardiac phenotypes includes determining whether the pediatric or adult congenital patient has a ventricular dysfunction, as well as the closely linked outcome of mortality.

[0010] In some aspects, determining one or more of the plurality of cardiac phenotypes includes determining whether the pediatric or adult congenital patient has ventricular dilation.

[0011] In some aspects, determining one or more of the plurality of cardiac phenotypes further includes assigning a qualitative assessment for the one or more of the plurality of cardiac phenotypes, wherein the qualitative assessment indicates whether a severity of a cardiac condition indicated by a cardiac phenotype.

[0012] In some aspects, determining the one or more of the plurality of cardiac phenotypes based on the received ECG data includes determining the one or more of the plurality of cardiac phenotypes based on one or more of: at least one ECG raw waveform, a QRS interval, a QRS axis, a T axis, a P axis, a PR interval, a QT interval, a QT corrected for heart rate (QTc), or a heart rate. [0013] In some aspects, determining using the one or more trained models includes determining using the one or more trained models were trained with training data that did not include data for pediatric patients having congenital heart disease. [0014] In some aspects, determining using the one or more trained models includes determining using the one or more trained models were trained with training data including data for pediatric or adult patients having congenital heart disease.

[0015] In some aspects, determining using the one or more trained models includes determining using one or more trained models were trained with training data for patients across a plurality of age ranges, each of the plurality of age ranges being a range within an overall age range of 0 to 18 years for pediatric patients, and greater than 18 years for adult congenital patients.

[0016] In some aspects, the plurality of age ranges include two or more of: 0 years to 1 year, 1 year to 3 years, 3 years to 8 years, 8 years to 12 years, 12 years to 18 years, or greater than 18 years.

[0017] In some aspects, the plurality of age ranges include two or more of: infancy, toddlerhood, school age, preadolescence, adolescence, and adults.

[0018] In some aspects, determining using the one or more trained models includes determining using one or more trained models were trained with training data including age data and/or sex data from the plurality of prior pediatric or adult congenital patients, wherein each of the age data and/or the sex data is combined in the training with the ECG data from the plurality of prior pediatric or adult congenital patients.

[0019] In some aspects, determining using the one or more trained models includes determining using one or more trained models were trained with training data including echocardiogram data from the plurality of prior pediatric or adult congenital patients, wherein the echocardiogram data is combined with the ECG data from the plurality of prior pediatric or adult congenital patients.

[0020] In some aspects, the echocardiogram data of the training data includes one or more of a ventricular ejection fraction EF, a ventricular mass, a ventricular mass/volume, a ventricular end- diastolic volume.

[0021] In some aspects, the mortality data of the training data includes death ages for pediatric and adult congenital patients obtained from an institutional database.

[0022] In some aspects, determining using one or more trained models trained with ECG data from the plurality of prior pediatric or adult congenital patients includes determining using one or more trained models trained with fdtered ECG data for the plurality of prior pediatric or adult congenital patients, the fdtered ECG data including noise exceeding a threshold.

[0023] In some aspects, the techniques described herein relate to a method of predicting a cardiac condition in a pediatric or adult congenital patient, the method including: determining, based on received ECG data from a pediatric or adult congenital patient, a prediction of whether the pediatric or adult congenital patient has any of one or more cardiac conditions, the determining the prediction including analyzing the received ECG data using one or more trained models, wherein the one or more trained models have been trained with ECG data from a plurality of prior pediatric or adult congenital patients; and outputting the prediction of whether the pediatric or adult congenital patient has any of the one or more cardiac conditions.

[0024] In some aspects, the techniques described herein relate to a method of predicting cardiac conditions in pediatric or adult congenital patients, the method including: determining, based on first received ECG data from a first pediatric or adult congenital patient, a first prediction of whether the first pediatric or adult congenital patient has one or more cardiac conditions, the determining including analyzing the first received ECG data using a trained model; determining, based on second received ECG data from a second pediatric or adult congenital patient, a second prediction of whether the second pediatric or adult congenital patient has the one or more cardiac conditions, the determining including analyzing the second received ECG data using the trained model; and outputting the first prediction and the second prediction for the first pediatric or adult congenital patient and the second pediatric or adult congenital patient, wherein the trained model was trained with training data including ECG data and cardiac condition data from a plurality of prior pediatric or adult congenital patients including patients from multiple age ranges of pediatric or adult congenital patients, the multiple age ranges including at least one adolescent or adult age range and at least one preadolescent age range, and wherein the first pediatric or adult congenital patient has an age in the at least one adolescent or adult age range and the second pediatric or adult congenital patient has an age in the at least one preadolescent age range.

[0025] In some aspects, the techniques described herein relate to a system for predicting a cardiac condition in a pediatric or adult congenital patient, the system including: an ECG monitoring device; a controller including at least one processor, at least one storage medium having encoded thereon executable instructions that, when executed by the at least one processor, cause the at least one processor to carry out a method including: determining, based on received ECG data from a pediatric or adult congenital patient, information regarding whether the pediatric or adult congenital patient has one or more cardiac conditions, the determining including analyzing the received ECG data using one or more trained model, and wherein the one or more trained model has been trained with training data including ECG data from a plurality of prior pediatric or adult congenital patients; and outputting the information regarding whether the pediatric or adult congenital patient has the one or more cardiac conditions.

[0026] In some aspects, the techniques described herein relate to at least one storage medium having encoded thereon executable instructions that, when executed by at least one processor, cause the at least one processor to carry out a method including: determining, based on received ECG data from a pediatric or adult congenital patient, information regarding whether the pediatric or adult congenital patient has one or more cardiac conditions, the determining including analyzing the received ECG data using one or more trained model, and wherein the one or more trained model has been trained with training data including ECG data from a plurality of prior pediatric or adult congenital patients; and outputting the information regarding whether the pediatric or adult congenital patient has the one or more cardiac conditions.

[0027] Methods and systems of the presently disclosed embodiments were isolated or otherwise manufactured in connection with the examples provided below. Other features and advantages will be apparent from the detailed description, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures is represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings::

[0029] FIG. 1 is a block diagram of a system with which some embodiments may operate for analyzing ECG data for a pediatric or adult congenital patient with respect to a cardiac phenotype; [0030] FIGS. 2A-2B are flowcharts of processes that may be implemented in some embodiments to evaluate information for identifying features related to cardiac phenotypes using ECG data from a pediatric or adult congenital patient, and for training one or more models to predict a cardiac condition, respectively;

[0031] FIG. 3 is a block diagram of a computing device with which some embodiments may operate;

[0032] FIG. 4 is a STROBE diagram showing initial patient selection, filtering at each data processing stage, patient partitioning, and primary outcome rates. Abbreviations: quality control (QC); congenital heart disease (CHD); left ventricle (LV);

[0033] FIG. 5 shows pediatric electrocardiogram-based deep learning model performance during internal testing. Performance of the artificial intelligence-enhanced pediatric electrocardiogram (AI-pECG; blue) and AI-pECG with age and sex (AI-pECG + age + sex; orange) model performances evaluated during internal testing with receiver operating (left) and precision-recall (right) curves for the: (A) left ventricular (LV) composite; (B) LV dysfunction; (C) LV hypertrophy; and (D) LV dilation outcomes. In panel C, the grey dot represents the benchmark of pediatric electrophysiologist (EP) expert ECG-based diagnosis of LV hypertrophy. AUROC and AUPRC metric values for each model and outcome are inset. Dotted line represents chance. 95% confidence intervals are shown using bootstrapping. Abbreviations: positive predictive value (PPV);

[0034] FIG. 6 shows model performance for single random ECGs per patient during internal testing. Performance of the artificial intelligence-enhanced pediatric electrocardiogram (AI-pECG; blue) and AI-pECG with age and sex (AI-pECG + age + sex; orange) model performances evaluated during internal testing on single random ECGs per patient with receiver operating curves for the: (A) left ventricular (LV) composite; (B) LV dysfunction; (C) LV hypertrophy; and (D) LV dilation outcomes. AUROC metric values for each model and outcome are inset. Dotted line represents chance. 95% confidence intervals are shown using bootstrapping;

[0035] FIG. 7 shows pediatric electrocardiogram-based deep learning model performance during testing in the emergency department. Performance of the artificial intelligence -enhanced pediatric electrocardiogram (AI-pECG; blue) and AI-pECG with age and sex (AI-pECG + age + sex; orange) model performances evaluated in a specific clinical setting (emergency department) with receiver operating (left) and precision-recall (right) curves for the: (A) left ventricular (LV) composite; (B) LV dysfunction; (C) LV hypertrophy; and (D) LV dilation outcomes. In panel C, the grey dot represents the benchmark of pediatric electrophysiologist (EP) expert ECG-based diagnosis of LV hypertrophy. AUROC and AUPRC metric values for each model and outcome are inset. Dotted line represents chance. 95% confidence intervals are shown using bootstrapping. Abbreviations: positive predictive value (PPV);

[0036] FIG. 8 shows model performance for single random ECGs per patient during testing in the emergency department. Performance of the artificial intelligence-enhanced pediatric electrocardiogram (AI-pECG; blue) and AI-pECG with age and sex (AI-pECG + age + sex; orange) model performances evaluated during testing on single random ECGs per patient with receiver operating curves for the: (A) left ventricular (LV) composite; (B) LV dysfunction; (C) LV hypertrophy; and (D) LV dilation outcomes. AUROC metric values for each model and outcome are inset. Dotted line represents chance. 95% confidence intervals are shown using bootstrapping; [0037] FIG. 9 shows model performance to predict quantitative cutoffs of left ventricular function and remodeling. Performance of the artificial intelligence-enhanced pediatric electrocardiogram (AI-pECG) algorithm evaluated in the internal (left) and emergency department (right) cohorts using receiver operating curves for the following outcomes: (A) left ventricular (LV) composite outcome (LV ejection fraction (LVEF) z-score < -X or LV mass z-score > X or LV end-diastolic volume (LVEDV) z-score > X, where cutoff X = 2.5 (black), 4.0 (blue), 6.0 (red); (B) LV dysfunction (LVEF z-score < -X); (C), LV hypertrophy (LV mass z-score > X); and (D) LV dilation (LVEDV z-score > X). AUROC metric values for each model and outcome are inset. Dotted line represents chance. 95% confidence intervals are shown using bootstrapping;

[0038] FIG. 10 shows model performance in age and sex subgroups. Forest plot showing AI- pECG area under the receiver operating curve (AUROC) performance when stratifying by age (age < 1, 1 < age < 3, 3 < age < 8, 8 < age < 12, age > 12) and sex for the following outcomes: left ventricular (LV) composite, LV dysfunction, LV hypertrophy, and LV dilation. 95% confidence intervals are shown using bootstrapping;

[0039] FIG. 11 shows explainability of AI-pECG predictions. Visualization of median waveforms generated in each lead using ECGs from the 100 highest (red) and 100 lowest (green) AI-pECG predictions of left ventricular (LV) dysfunction, hypertrophy, and dilation. Saliency mapping demarcates regions of the ECG waveform having greatest (dark blue) and least (light blue) influence on each outcome. Saliency was averaged over the 100 highest predicted ECGs for each outcome.

[0040] FIG. 12 shows explainability of AI-pECG predictions for the composite outcome. Visualization of median waveforms generated in each lead using ECGs from the 100 highest (red) and 100 lowest (green) AI-pECG predictions of the composite outcome. Saliency mapping demarcates regions of the ECG waveform having greatest (dark blue) and least (light blue) influence on the composite outcome. Saliency was averaged over the 100 highest predicted ECGs for the composite outcome.

[0041] While the above-identified drawings set forth presently disclosed embodiments, other embodiments are also contemplated, as noted in the discussion. This disclosure presents illustrative embodiments by way of representation and not limitation. Numerous other modifications and embodiments can be devised by those skilled in the art which fall within the scope and spirit of the principles of the presently disclosed embodiments.

DETAILED DESCRIPTION

[0042] Disclosed herein are techniques for evaluating cardiac health of a pediatric or adult congenital patient, including techniques for generating one or more models (e.g., machine learning models) trained to predict a cardiac condition in a pediatric or adult congenital patient. Some methods may include receiving electrocardiogram (ECG) data of a pediatric or adult congenital patient, determining whether the patient corresponds to one or more of multiple cardiac phenotypes, each corresponding to a presence of a cardiac condition based on analysis by one or more trained models, and outputting the cardiac phenotype(s) based on the ECG data. In some embodiments, the cardiac condition may include one or more of mortality, ventricular dysfunction, ventricular dilation, and ventricular hypertrophy. In embodiments that use one or more trained models, the trained model(s) may be trained using training data that includes ECG data from multiple prior pediatric or adult congenital patients and information indicating whether each prior patient had one or more cardiac conditions.

[0043] ECG is a quantitative method that indicates pulse waveforms in cardiac screening. While ECG has been used for some forms of cardiac screening, ECG has not been reliable for cardiac phenotyping or diagnostics of cardiac conditions based on a specific correlation between waveforms and cardiac phenotypes, due to variability in waveforms. As such, for cardiac phenotyping to diagnose a cardiac condition, a patient has conventionally relied on MRI or on echocardiograms instead. These techniques have high reliability and have been in wide use, and clinicians therefore prefer to use these techniques. Unfortunately, availability of this equipment and diagnostic technique can be limited and have a high cost, particularly in smaller clinic settings or remote areas. This means echocardiogram or MRI may not be available for some patients at all (where the technology is not present) or may not be available to certain patients (e.g., where limited availability imposes cost or other hurdles to use). The inventors have recognized that there would be advantages to techniques for cardiac phenotyping and diagnostics using other techniques.

[0044] Some recent work has been performed in experimenting with deep learning-based artificial intelligence-enhanced ECG (AI-ECG) algorithms for use in diagnostics for certain cardiac conditions. AI-ECG studies have been explored for use in adult populations, such as to predict a range of general adult cardiovascular conditions. However, existing methods show variability in diagnostic efficacy, raising challenges for use. Further, previous studies have primarily focused on adults with acquired cardiovascular diseases and have not been directed to adults with congenital heart disease, generally due to a number of reasons including, for example, insufficient data for testing and a lack of likelihood that their methods would be similarly applicable. Regarding the latter, in particular, there are ECG signatures in adult congenital heart disease that are heterogeneous and completely distinct from the general adult population (e.g., adults with acquired cardiovascular diseases), making it nonobvious that an AI-ECG model would function successfully in this population. Therefore, it is reasonable to expect that existing AI-ECG models for the general adult population would not translate well for the adult congenital heart disease populations.

[0045] While there has been some early work on use of ECG in adult cardiovascular diagnostics, it has not been explored for pediatric or adult congenital use, including based on significant anatomical differences between adult and pediatric or adult congenital patient populations and within pediatric or adult congenital patient populations. These significant anatomical differences have suggested that even if ECG were ultimately be found to be a viable diagnostic tool for adult populations, ECG would not necessarily be successful as a diagnostic for pediatric and adult congenital populations. For example, the epidemiology and patterns of normal versus abnormal pediatric and adult congenital ECG waveforms differ significantly from those of structurally normal adult heart, which prevents applicability of adult AI-ECG algorithms to pediatric and adult congenital cohorts. Further, there are well-known progressive anatomical and physiological changes occurring from birth to adolescence leading to more age-dependent variations in pediatric ECGs than in adult ECGs, preventing applicability of observable changes in one age population to other age populations even within a pediatric group.

[0046] ECG data is not traditionally used to deeply phenotype the cardiac health of pediatric and adult congenital population, thus any implementation of Al-enhanced algorithms would be expected to exhibit similar unreliability due to the anatomical data on which such algorithms would rely for training. This prevents the ability to understand ECG data across multiple age ranges, in particular those in the pediatric age range, to provide guidance for how to selectively identify whether a pediatric or adult congenital patient may be exhibiting a cardiac phenotype, such as those mentioned above, that are indicative of a cardiac condition. Even if one were to apply an AI-ECG model to pediatric and adult congenital cohorts, the variability across age ranges such as infancy and adolescence would prevent reliability of such a model to any individual within pediatric cohorts, i.e., from 0 to 18 years of age. The presence of congenital heart disease provides an additional underlying hurdle of unreliability, as the abnormalities in the heart of a pediatric or adult congenital patient - which may vary from mild to severe - results in even further variability in the resulting ECG waveforms. As a result, there are relatively few available AI-ECG applications to pediatric and adult congenital cardiology, highlighting the paucity of pediatric AI-ECG models to date that could benefit both resource-rich and resource limited pediatric and adult congenital settings.

[0047] The inventors have recognized and appreciated, however, that an ECG-based analysis for pediatric or adult congenital patients would be advantageous, including in healthcare scenarios where MRI or echocardiogram equipment may be unavailable or difficult to obtain or use. The inventors determined, for example, that training a machine learning model for use with pediatric and adult congenital ECG data (sometimes termed herein an “AI-pECG model”) with ECG data paired with echocardiogram data and/or MRI data from pediatric populations may be used to predict cardiac phenotypes for pediatric or adult congenital patients. Such cardiac phenotypes may include risk of mortality, LV dysfunction, hypertrophy, and dilation. Echocardiograms are highly useful for diagnosing various cardiac conditions, but are associated with high costs and may be referred only when necessary. Further, while previous diagnostic tools have been useful for identifying particular cardiac conditions in isolation, there are no tools available for pediatrics in determining such conditions either individually or as a composite outcome. In this context, a composite outcome would include a prediction of at least two of the aforementioned phenotypes. [0048] The inventors have further recognized and appreciated the value of obtaining dedicated and age-delineated data sets that allow training for each respective age group within pediatric populations. In particular, for one experiment, the inventors accumulated nearly 100,000 ECG- echo pairs < 2 days apart, an independent internal test set of >20,000 ECG-echo pairs, as well as in a test set of patients in the emergency room with >3,000 ECG-echo pairs. Subgroup analysis was also performed in this experiment, using age partitioning with groupings of age < 1, 1 < age < 3, 3 < age < 8, 8 < age < 12, and 12 < age < 18 years, matching groupings related to infancy, toddlerhood, school age, preadolescence, and adolescence.

[0049] The inventors recognized and appreciated the value of filtering training data to eliminate some cardiac phenotypes. For example, some embodiments may include data filtering that excludes data (e.g., ECG waveforms with poor quality data; patients with major congenital heart disease based on their electronic health record) from pediatric patients with major congenital heart disease, thus ensuring models are trained without information for patients with major congenital heart disease. This may prevent a model from predicting presence of major congenital heart disease, but may have reliability advantages for predicting other cardiac phenotypes.

[0050] Human expert interpretation of echocardiograms of LV function and remodeling is particularly useful for training an AI-pECG model, given -25% of the accumulated patient data are without quantitative measures (substantially reducing available training datasets for the AI- pECG), and a human expert opinion incorporates multiple clinical datapoints that aid in decision making for use in training the model. However, because qualitative and quantitative cutoffs individually have their own drawbacks, and most emergency medicine physicians will attempt cardiac point-of-care ultrasound and report qualitative outcomes on function, the inventors wanted to develop a model that worked regardless of a cutoff method. Some embodiments described herein may aid in obtaining data with greater accuracy and quality for training a machine learning model without such cutoffs, which may increase a reliability thereof.

[0051] The inventors have further recognized and appreciated the clinical utility and convenience of AI-pECG modeling. Specifically, some models described herein may be performed with cheap and rapidly generated ECG waveform data. The robust performance of models suggests they are at least partially resistant to noise generated from obtaining data. Importantly, in some embodiments, each model may implement only a single modality (i.e., independent of demographics such as age/sex), rather than a complex clinical scoring system that would require user interaction and may be susceptible to input error.

[0052] It should further be noted there is a relative lack of scoring systems for ventricular dilation or dysfunction in pediatric or adult congenital patients, thus enhancing the need for such a model. The inventors have further identified through saliency mapping as to regions of pediatric ECG waveforms that influence model predictions and provide novel insight into clinicians detecting risk of mortality or LV dysfunction and remodeling. In particular, the use of saliency mapping in some implementations may enable visual comparison of ECGs with existing algorithms for ECG interpretation, with outcomes, both for individual and composite phenotype predictions, being representing of an underlying outcome. The inventors have appreciated, for example, that to predict LV hypertrophy, salient features in some cases may be or include precordial and limb lead I QRS complexes, with deep S waves in V1-V2 and high amplitude R waves in limb lead I included as predictive high-risk features. Further, 1 lor LV dilation, the salient features in some cases may be or include lateral precordial (V4-V6) QRS complexes, with high amplitude R waves in V4-V6 included as predictive high-risk features. Saliency mapping for the composite outcome was observed to merge features from each of the individual outcomes of interest, i.e., LV hypertrophy and LV dilation.

[0053] The inventors have further recognized and appreciated the utility of an AI-pECG in an emergency room, expanding additional clinical settings that would benefit from such tools. The economic burden of cardiac conditions, when addressed in emergency rooms, includes the potential for misdiagnosis leading to unnecessary referrals and associated costs of echocardiograms, as well as missed potentially fatal diagnoses. AI-pECG predictions could help guide an emergency physician’s need to consult a pediatric cardiologist, and could help guide a pediatric cardiologist’s decision of if and/or when to get an echocardiogram for a child without congenital heart disease. This democratization of specialty expertise is likely to be particularly valuable for hospitals with low pediatric volumes and/or limited pediatric cardiology experience. [0054] Techniques described herein may be useful in some embodiments in generating a model to output one or more cardiac phenotypes determined for a pediatric or adult congenital patient based on received ECG data. Further described herein are examples of techniques and systems with which such techniques may be used. These include, for example, (1) systems with which some embodiments of the methods described herein may operate; (2) methods for conducting training of at least one model using information regarding one or more cardiac phenotypes corresponding to a presence of a cardiac condition; (3) methods of identifying one or more cardiac phenotypes predicted by the at least one model; and (4) methods for training a model using additional data or information from the pediatric or adult congenital patient, such as echocardiogram data, age range, and/or sex, to detect a cardiac condition or a composite of multiple cardiac conditions predicted by the model to be indicative of one or more cardiac conditions.

[0055] The following description and examples illustrate in detail some embodiments of techniques and technologies described herein. It is to be understood that embodiments are not limited to acting in accordance with the specific examples provided herein, as other approaches are possible. Those of skill in the art will recognize that there may be variations and modifications from the specific examples below that are within the scope of this disclosure.

Illustrative Systems [0056] FIG. 1 illustrates a block diagram of a system 100 with which some embodiments may operate. The system 100 can evaluate a patient(s) to determine whether his or her respective phenotype may be normal/healthy or whether the phenotype corresponds to a risk of a particular cardiac condition, such as future mortality, ventricular dysfunction, ventricular dilation, and ventricular hypertrophy. The system 100 may in some embodiments produce the estimate of risk by analyzing a combination of pECG data with a trained model, where the trained model may be trained on prior pECG data. Such prior pECG data may be or include those from previous pediatric or adult congenital patients.

[0057] The system 100 can include a patient 102. In some embodiments, the patient 102 may be healthy, or has symptoms of a cardiac phenotype corresponding to the presence of a cardiac condition such that testing may be done to determine whether the patient 102 has the cardiac condition. For example, the patient 102 can be a recipient of health care services that are administered by healthcare professionals. For example, the patient 102 can be ill or injured and require treatment. The patient 102 can seek the advice of healthcare professionals regarding treatment, which may, for example, be a response to pain or a feeling of unease. Accordingly, one or more clinicians 104 may interface with the patient 102 to manage illness or injury of the patient. Examples of clinicians 104 include a physician, nurse, physician assistant, nurse practitioner, psychologist, clinical pharmacist, clinical scientist, or specialist physician such as an electrophysiologist.

[0058] One or more ECG recordings 106 may be obtained from the patient 102, such as by the clinician 104 obtaining the sample through an ECG monitor 107 from the patient 102, though it should be appreciated that embodiments are not so limited. In some embodiments, the ECG recording 106 may be sufficient to produce signal data for the patient 102. More generally, the ECG recording 106 can be gathered from the patient to aid in medical diagnostics or evaluation of treatment. The ECG recording 106 can be obtained prior to use, which can include receiving from a database.

[0059] The system 100 can include a pECG data analysis facility 116, which may be or include one or more tools for analyzing the ECG recording 106. The exact form of the pECG data analysis facility 116 may depend on the form of the ECG recording 106 taken from the patient. Examples of analytical tools for detecting markers were discussed above, any one, two, or more of which may be implemented in some embodiments. The pECG data analysis facility 116 may also include a signal analyzer and/or an ECG analysis module.

[0060] The system 100 can include a client computing device 110, which may be a desktop or laptop personal computer, smart mobile phone, server, or other suitable device. The client computing device 110 may include a client interface 112 by which the patient 102 or the clinician 104 may interact with the client computing device 110. For example, the patient 102 or the clinician 104 can use the client interface 112 to interface with the pECG data analysis facility 116 of the server computing device 114. For example, the patient 102 and/or clinician 104 may operate the client interface 112 to initiate analysis of the ECG recording 106 by the pECG data analysis facility 116 and display analysis results such as whether features were detected and/or levels of those features in the interface 112. The patient 102 and/or clinician 104 may additionally or alternatively operate the client interface 112 to input features and/or feature levels obtained from the pECG data analysis facility 116, such as output to the patient 102 and/or clinician 104 in another interface. Those values may be provided to the pECG data analysis facility 116. As a further example, the patient 102 and/or clinician 104 may operate the client interface 112 to initiate analysis of the ECG recording 106 by the pECG data analysis facility 116. Results of analysis of the results (received from the interface 112) by the pECG data analysis facility 116 may be output to the client interface 112, such as by being received at the client interface 112 and displayed on the device 110. In some embodiments, as mentioned above, the client interface 112 may include a web interface, such as one or more web pages into which values may be output and which may display results of the analysis by the pECG data analysis facility 116, but embodiments are not so limited. The client interface 112 may accept input in a variety of different formats, such as through speech recognition, text input, or other means, as embodiments are not limited in this respect.

[0061] The system 100 can include a server computing device 114, which may include a pECG data analysis facility 116 configured to analyze factors (e.g., derived from the ECG recording 106) for the patient 102 with one or more cardiac phenotype to determine a risk that the patient 102 has a cardiac condition. These factors may include demographic information such as age or sex of patient 102 and may include information represented in a numeric form or as change indicators, such as trajectory indications.

[0062] The system 100 can include a network 118 to facilitate communications among the pECG data analysis facility 116, the client computing device 110, and the server computing device 114. The network 118 can be or include any one or more wired and/or wireless, local- and/or wide-area network, including one or more enterprise networks and/or the Internet.

[0063] While the example of FIG. 1 includes the client interface on a device 110 separate from the trained pECG analyzer 112, it should be appreciated that embodiments are not so limited. In other embodiments, the client interface 112 may be an interface of the pECG data analysis facility 116 and may be operated by the patient 102 and/or the clinician 104. Additionally or alternatively, while the pECG data analysis facility 116 is illustrated on a different computing device from the client computing device 110, embodiments are not so limited. In some embodiments, the client interface 112 may not be separate from the pECG data analysis facility 116, but instead may be implemented as a single program or software application. In some embodiments, a pECG data analysis facility 116 may include the client interface 112, and the interface 112 and facility 116 may be implemented within the same program or application executed on the pECG data analysis facility 116.

Example Methods of predicting a cardiac condition

[0064] FIGS. 2A-2B flowcharts of processes 1000 and 2000 that may be implemented in some embodiments to predict a cardiac condition and train a model for predicting a cardiac condition, respectively. Processes 1000 and 2000 can be implemented in some embodiments by the pECG data analysis facility 116 of the server computing device 114, which can output one or more cardiac phenotypes that satisfy predetermined criteria for the ECG data.

[0065] In step 1001, the pECG data analysis facility 116 receives ECG data from a pediatric or adult congenital patient, referred to herein as pECG data. The pECG data may include one or more of ECG recordings 106 and stored ECG data on a computer-readable storage medium. In some embodiments, the pECG data includes raw ECG signals exported from an ECG data management system. For example, the raw ECG signals may include waveform data, and the waveform data may include a vector of data sampled at a rate of 250 Hz for 10 seconds duration (2500 samples) corresponding to a lead (I, II, and V1-V6). The vector of data may be onedimensional. The vector of data may be linearly transformed. In some embodiments, the transformation is based on the Einthoven law and/or Goldberger equation, and may be used to obtain leads III, aVF (augmented vector foot), aVL (augmented vector left), and aVR (augmented vector right). For example, the waveform data includes voltages that have been one or more of filtered and digitized for sampling and linear transformation. Given that ECGs are prone to recording errors (e.g., baseline wander; electrical interference), a high pass filter may be utilized. In some embodiments, the high pass filter may include a cutoff frequency of about 0.8 Hz, a rejection band of about 0.2 Hz, a ripple in a passband of about 0.5 dB, and an attenuation in a rejection band of about 40 dB. In some embodiments, the ECG may be trimmed to facilitate conveniently working with convolution neural networks. For example, the ECG data may be trimmed to about 2048 samples (approximately 8 seconds). In addition to analyzing pECG data for a patient, the pECG data analysis facility 116 may analyze demographic data for corresponding patients. Such demographic data may include age of the patient, sex of the patient, socioeconomic information regarding the patient, or other demographic information. The pECG data may include measurements including one or more of QRS interval, QRS axis, T axis, P axis, PR interval, QT interval, QTc, and heart rate. [0066] In step 1002, the pECG data analysis facility 116 analyzes the pECG data (and, in some embodiments, demographic data or other patient data) using at least one trained model disclosed herein, wherein the at least one trained model may have been trained according to process 2000 described in more detail below. The pECG data may be sent to the same trained model, regardless of the patient’s age or sex, as the model may be used for a variety of age ranges for pediatric or adult congenital patients. Based on the analysis in step 1002, one or more cardiac phenotypes may be determined, such that step 1003 includes assigning one or more cardiac phenotypes to the pediatric or adult congenital patient. In some embodiments, the one or more phenotypes include a normal (i.e., healthy) phenotype, a ventricular dysfunction phenotype, a ventricular dilation phenotype, a ventricular hypertrophy phenotype, and a risk of mortality phenotype. The one or more cardiac phenotypes are preprogrammed to correspond to a presence of a cardiac condition. The determining may include determining a probability of the pediatric or adult congenital patient having any of one or more cardiac conditions, or each of the one or more cardiac conditions. The determining may further include determining a probability of the pediatric or adult congenital patient having the one or more cardiac conditions based on the one or more cardiac phenotypes. The determining may also include assigning a qualitative assessment for each of the one or more cardiac phenotypes, such that the qualitative assessment indicates whether a severity of a cardiac condition is indicated by the cardiac phenotype.

[0067] In some embodiments, the probability of the pediatric or adult congenital patient having the one or more cardiac conditions may further be used to predict a mortality of the pediatric or adult congenital patient, such that the mortality is associated with one or more of the presence of the one or more cardiac condition and the probability of the pediatric or adult congenital patient having the one or more cardiac condition. In some embodiments, the model may be trained to directly predict the risk of mortality of the pediatric or adult congenital patient.

[0068] The determining may include determining a probability of the pediatric or adult congenital patient having each of the one or more cardiac conditions, such that each cardiac condition may individually be associated with its own probability. The cardiac conditions may include one or more of ventricular dysfunction, ventricular dilation, and ventricular hypertrophy, where each may be associated with either a left or right ventricle. Determining the one or more cardiac phenotype may include comparing the probability of the patient having each of the one or more cardiac conditions to a threshold. The threshold for each of the one or more cardiac condition may be determined by the at least one trained model. Finally, step 1003 includes outputting the one or more cardiac phenotypes.

[0069] Process 2000 may be used to generate the at least one trained model used in step 1002 of process 1000. In step 2001, the pECG data analysis facility 116 receives training data from one or more prior pediatric or adult congenital patients. Different training data may be used for training of the one or more trained models. For example, the training data may include ECG data, i.e., training pECG data. The training pECG data may further include ECG data from prior pediatric or adult patients with congenital heart disease. In some embodiments, the training pECG data may be fdtered to exclude ECG data from prior pediatric patients with congenital heart disease. In some embodiments, the training pECG data may be ECG data that is fdtered of noise exceeding one or more thresholds.

[0070] The training data may also include echocardiogram data associated with the ECG data to form ECG-echo pairs. The echocardiogram data may include one or more of a ventricular ejection fraction EF, a ventricular mass, a ventricular mass/volume, and a ventricular end-diastolic volume. The training data may also include MRI data associated with the ECG data to form ECG-MRI pairs. The prior pediatric or adult congenital patients may be stratified across one or more age ranges, such that each of the one or more age ranges are between 0 and 18 years in being consistent with a pediatric cohort, or greater than 18 years for the adult congenital cohort. For example, the one or more age ranges may include two or more of: 0 years to 1 year, 1 year to 3 years, 3 years to 8 years, 8 years to 12 years, or 12 years to 18 years. In some embodiments, the one or more age ranges includes two or more of: infancy, toddlerhood, school age, preadolescence, adolescence, and adults in the case of adult congenital patients. The one or more prior pediatric or adult congenital patients may further be stratified by their sex.

[0071] In step 2002, the pECG data analysis facility 116 receives information as to whether each of the prior pediatric or adult congenital patients had one or more of the cardiac conditions. The prior pediatric or adult congenital patients may include a probability of each of one or more cardiac phenotypes associated with one or more cardiac condition. The prior pediatric or adult congenital patients may also include a probability of a composite of each of the one or more cardiac phenotypes.

[0072] In step 2003, the pECG data analysis facility 116 trains at least one model to determine at least one cardiac phenotype using the training data and the information. In some embodiments, the training may incorporate one or more of the sex and the age of each of the prior pediatric or adult congenital patients. Further, each of the age data and/or the sex data is combined in the training with the pECG data from the prior pediatric or adult congenital patients. The training may also combine the echocardiogram data with the pECG data from the prior pediatric or adult congenital patients.

[0073] In some embodiments, process 2000 may further include evaluating a performance of the at least one trained model, wherein the evaluating may include comparing outputs of the at least one trained model to expert diagnoses of each of the prior pediatric or adult congenital patients for the one or more cardiac conditions. The evaluating may further include comparing a sensitivity of the at least one trained model to the expert diagnoses. Each of the expert diagnoses may include one or more qualitative and/or quantitative cutoffs for establishing a diagnosis.

Some Machine Learning Model Implementations

[0074] As discussed above, the pECG analysis facility 116 may employ various machine learning techniques to identify features in the ECG recordings 106 regarding cardiac phenotypes. These machine learning techniques may, for example, leverage models of the features to be identified in the information. These models may be constructed using various supervised and/or unsupervised learning techniques. In supervised learning, the pECG analysis facility 116 may train a model using training data including the output features to be identified (e.g., LV dysfunction, LV hypertrophy, LV dilation). For example, an supervised learning model includes receiving training data, receiving annotations in the received training data, and training the model using the annotated training data.

[0075] The training data may include, for example, training data that includes the feature(s) that the model will be trained to recognize. For example, the pECG analysis facility may be constructing a model to be used for recognizing LV dysfunction or remodeling on echocardiogram. Similar training can be performed to recognize LV dysfunction or remodeling on MRI.

[0076] The pECG analysis facility 116 may train the model using the annotated training data. In some embodiments, the pECG analysis facility 116 may train the model to identify the features marked in the annotated training data. For example, the training data may include LV dysfunction or remodeling on echocardiogram. In this example, the pECG analysis facility 116 may be trained to identify suspected features indicative of cardiac phenotypes using the annotated training data.

[0077] Any of a variety of machine learning algorithms may be used in implementing the disclosed image processing and analysis methods. For example, the machine learning algorithm employed may include a supervised learning algorithm, an unsupervised learning algorithm, a semisupervised learning algorithm, a deep learning algorithm, or any combination thereof. In some embodiments, the machine learning algorithm employed may include an artificial neural network algorithm, a Gaussian process regression algorithm, a logistical model tree algorithm, a random forest algorithm, a fuzzy classifier algorithm, a decision tree algorithm, a hierarchical clustering algorithm, a k-means algorithm, a fuzzy clustering algorithm, a deep Boltzmann machine learning algorithm, a deep convolutional neural network algorithm, a deep recurrent neural network, or any combination thereof, some of which will be described in more detail below.

[0078] As noted above, the machine learning algorithm(s) employed in the disclosed methods and systems for characterizing ECG recordings 106 may include a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a deep learning algorithm, etc., or any combination thereof.

[0079] Having described example processes, it should be appreciated that various alternations may be made to the described processes without departing from the scope of the present disclosure. For example, various acts may be omitted, combined, repeated, or added. Further, the acts in any of the processes described herein do not need to be performed in the particular order shown.

Illustrative computer implementations

[0080] Techniques operating according to the principles described herein may be implemented in any suitable manner. Included in the discussion above are a series of flow charts showing the steps and acts of various processes that analyze marker data, such as ECG features and other data, for one or more patients expressing cardiac phenotypes to determine features that may be indicative of a cardiac condition. The processing and decision blocks of the flow charts above represent steps and acts that may be included in algorithms that carry out these various processes. Algorithms derived from these processes may be implemented as software integrated with and directing the operation of one or more single- or multi-purpose processors, may be implemented as fimctionally- equivalent circuits such as a Digital Signal Processing (DSP) circuit or an Application- Specific Integrated Circuit (ASIC), or may be implemented in any other suitable manner. It should be appreciated that the flow charts included herein do not depict the syntax or operation of any particular circuit or of any particular programming language or type of programming language. Rather, the flow charts illustrate the functional information one skilled in the art may use to fabricate circuits or to implement computer software algorithms to perform the processing of a particular apparatus carrying out the types of techniques described herein. It should also be appreciated that, unless otherwise indicated herein, the particular sequence of steps and/or acts described in each flow chart is merely illustrative of the algorithms that may be implemented and can be varied in implementations and embodiments of the principles described herein.

[0081] Accordingly, in some embodiments, the techniques described herein may be embodied in computer-executable instructions implemented as software, including as application software, system software, firmware, middleware, embedded code, or any other suitable type of computer code. Such computer-executable instructions may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.

[0082] When techniques described herein are embodied as computer-executable instructions, these computer-executable instructions may be implemented in any suitable manner, including as a number of functional facilities, each providing one or more operations to complete execution of algorithms operating according to these techniques. A “functional facility,” however instantiated, is a structural component of a computer system that, when integrated with and executed by one or more computers, causes the one or more computers to perform a specific operational role. A functional facility may be a portion of or an entire software element. For example, a functional facility may be implemented as a function of a process, or as a discrete process, or as any other suitable unit of processing. If techniques described herein are implemented as multiple functional facilities, each functional facility may be implemented in its own way; all need not be implemented the same way. Additionally, these functional facilities may be executed in parallel and/or serially, as appropriate, and may pass information between one another using a shared memory on the computer(s) on which they are executing, using a message passing protocol, or in any other suitable way.

[0083] Generally, functional facilities include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the functional facilities may be combined or distributed as desired in the systems in which they operate. In some implementations, one or more functional facilities carrying out techniques herein may together form a complete software package. These functional facilities may, in alternative embodiments, be adapted to interact with other, unrelated functional facilities and/or processes, to implement a software program application, for example as a software program application such as a signal analysis facility.

[0084] Some exemplary functional facilities have been described herein for carrying out one or more tasks. It should be appreciated, though, that the functional facilities and division of tasks described is merely illustrative of the type of functional facilities that may implement the exemplary techniques described herein, and that embodiments are not limited to being implemented in any specific number, division, or type of functional facilities. In some implementations, all functionality may be implemented in a single functional facility. It should also be appreciated that, in some implementations, some of the functional facilities described herein may be implemented together with or separately from others (i.e., as a single unit or separate units), or some of these functional facilities may not be implemented.

[0085] Computer-executable instructions implementing the techniques described herein (when implemented as one or more functional facilities or in any other manner) may, in some embodiments, be encoded on one or more computer-readable media to provide functionality to the media. Computer-readable media include magnetic media such as a hard disk drive, optical media such as a Compact Disk (CD) or a Digital Versatile Disk (DVD), a persistent or non-persistent solid-state memory (e.g., Flash memory, Magnetic RAM, etc.), or any other suitable storage media. Such a computer-readable medium may be implemented in any suitable manner, including as computer-readable storage media 1106 of FIG. 3 described below (i.e., as a portion of a computing device 1100) or as a stand-alone, separate storage medium. As used herein, “computer-readable media” (also called “computer-readable storage media”) refers to tangible storage media. Tangible storage media are non-transitory and have at least one physical, structural component. In a “computer-readable medium,” as used herein, at least one physical, structural component has at least one physical property that may be altered in some way during a process of creating the medium with embedded information, a process of recording information thereon, or any other process of encoding the medium with information. For example, a magnetization state of a portion of a physical structure of a computer-readable medium may be altered during a recording process. [0086] In some, but not all, implementations in which the techniques may be embodied as computer-executable instructions, these instructions may be executed on one or more suitable computing device(s) operating in any suitable computer system, including the exemplary computer system of FIG. 3, or one or more computing devices (or one or more processors of one or more computing devices) may be programmed to execute the computer-executable instructions. A computing device or processor may be programmed to execute instructions when the instructions are stored in a manner accessible to the computing device or processor, such as in a data store (e.g., an on-chip cache or instruction register, a computer-readable storage medium accessible via a bus, a computer-readable storage medium accessible via one or more networks and accessible by the device/processor, etc.). Functional facilities including these computer-executable instructions may be integrated with and direct the operation of a single multi-purpose programmable digital computing device, a coordinated system of two or more multi-purpose computing device sharing processing power and jointly carrying out the techniques described herein, a single computing device or coordinated system of computing devices (co-located or geographically distributed) dedicated to executing the techniques described herein, one or more Field-Programmable Gate Arrays (FPGAs) for carrying out the techniques described herein, or any other suitable system. [0087] FIG. 3 illustrates one exemplary implementation of a computing device in the form of a computing device 1100 that may be used in a system implementing techniques described herein, although others are possible. It should be appreciated that FIG. 3 is intended neither to be a depiction of necessary components for a computing device to execute a pECG analysis facility in accordance with the principles described herein, nor a comprehensive depiction.

[0088] Computing device 1100 may include at least one processor 1101, a network adapter 1102, and computer-readable storage media 1103. Computing device 1100 may be, for example, a desktop or laptop personal computer, a personal digital assistant (PDA), a smart mobile phone, a server, a wireless access point or other networking element, or any other suitable computing device. Network adapter 1102 may be any suitable hardware and/or software to enable the computing device 1100 to communicate wired and/or wirelessly with any other suitable computing device over any suitable computing network. The computing network may include wireless access points, switches, routers, gateways, and/or other networking equipment as well as any suitable wired and/or wireless communication medium or media for exchanging data between two or more computers, including the Internet. Computer-readable media 1103 may be adapted to store data to be processed and/or instructions to be executed by processor 1101. Processor 1101 enables processing of data and execution of instructions. The data and instructions may be stored on the computer-readable storage media 1103.

[0089] The data and instructions stored on computer-readable storage media 1103 may include computer-executable instructions implementing techniques which operate according to the principles described herein. In the example of FIG. 3, computer-readable storage media 1103 stores computer-executable instructions implementing various facilities and storing various information as described above. Computer-readable storage media 1103 may store pECG data analysis facility 116.

[0090] While not illustrated in FIG. 3, a computing device may additionally have one or more components and peripherals, including input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computing device may receive input information through speech recognition or in other audible format.

Examples

[0091] Described below are examples of ways in which techniques described herein may be implemented. It should be appreciated that these examples are merely illustrative, that embodiments are not limited to operating in accordance with the specific examples shown in the figures and discussed below, and that other embodiments are possible.

Example 1: Study Population and Patient Assignment

[0092] The purpose of this example is to provide exemplary data that may be used for training a model described herein. Inclusion criteria for exemplary data consisted of children < 18 years old with at least one echo. Echos performed in the operating room, medical intensive care unit, or cardiac intensive care unit were excluded. Patients with known major congenital heart disease or implantable cardioverter-defibrillator/pacemaker were excluded. Patients with known major congenital heart disease based on a Fyler coding system were identified.

[0093] Each qualifying echo event was paired to an ECG; only ECG-echo pairs < 2 days apart were included. For patients with multiple ECGs within this timeframe, only the ECG closest in time to the echo was included. ECG-echo pairs with ECGs failing to pass quality control (see “Data Processing, Quality Control, and Filtering” for details) were removed. The remaining ECG-echo pairs were included as the main cohort.

[0094] A group stratified design was implemented for partitioning of the main cohort. Each patient was treated as a separate group, which restricts ECG-echo pairs for a given patient to either training or testing datasets in order to minimize leakage of ECG-echo pair data. If an ECG or echo within an ECG-echo pair was performed in the emergency department, then the ECG-echo pair was placed in the external setting group. These same patients with other ECG-echo pairs were forced into the internal testing group to ensure no data leakage occurred between training and testing. The remaining patients were then randomly partitioned 80:20 into training and internal testing datasets. [0095] Of the 272,221 echos from 104,508 children < 18 years old without congenital heart disease, there were 122,757 ECG-echo pairs < 2 days apart. Of these ECG-echo pairs, 119,787 ECGs (61,722 patients) passed quality control, thus forming the main study cohort (FIG. 4).

[0096] The training cohort comprised of 92,377 ECG-echo pairs (46,261 patients; median age 8.2 [IQR, 2.9 -13.8] years; 54% male), 8.2% with composite LV outcomes, 2.4% with LV dysfunction, 3.5% with LV hypertrophy, and 3.8% with LV dilation (TABLE 1). The internal testing cohort comprised of 24,343 ECG-echo pairs (12,631 patients; median age 8.3 [IQR, 3.0- 13.8] years; 54% male), 7.7% with composite LV outcomes, 2.4% with LV dysfunction, 3.2% with LV hypertrophy, and 3.5% with LV dilation (TABLE 1). The external testing cohort comprised of 3,067 ECG-echo pairs (2,830 patients; median age 8.0 [IQR, 1.4-14.6] years; 57% male), 10.0% with composite LV outcomes, 5.0% with LV dysfunction, 3.5% with LV hypertrophy, and 4.4% with LV dilation (TABLE 1). Further details on demographics, ECG, and echo characteristics are summarized in TABLE 1.

[0097] TABLE 1: Comparison of Demographics, ECG and Echo Characteristics, and Outcomes Stratified by Study Cohorts

[0098] Data presented as median (interquartile range).

[0099] Abbreviations: left ventricle (LV); beats per minute (BPM); ejection fraction (EF).

[00100] Patient characteristics and outcomes stratified by age group are shown in TABLE 2. ECG characteristics stratified by age are within range of previously reported values for healthy children, and confidence intervals for echo z-scores include zero. ECG findings by age include a more rightward QRS, T, and P axis for age < 1, an increasing PR and QT interval with age, and decreasing heart rate with age (TABLE 2).

[00101] TABLE 2: Comparison of Demographics, ECG and Echo Characteristics, and

Outcomes Stratified by Study Cohorts

[00102] Data presented as median (interquartile range). Age in years.

[00103] Abbreviations: left ventricle (LV); beats per minute (BPM); ejection fraction (EF).

[00104] Tables 3-6 highlight the numerous significant differences in ECG-echo pair demographics, ECG characteristics, and echo data when stratifying by each outcome. Of note, approximately 40% of patients with LV dysfunction had concomitant LV dilation (TABLE 4), approximately 10% of patients with LV hypertrophy had concomitant LV dilation (TABLE 5). Approximately 30% and 10% of patients with LV dilation also had LV dysfunction and hypertrophy, respectively (TABLE 6).

[00105] TABLE 3 : Comparison of ECG-Echo Pair Demographics, ECG Characteristics, and Echo Characteristics Stratified by Composite Outcome

[00106] Data presented as median (interquartile range). Age in years. P-values obtained by Wilcoxon rank sum test, Fisher's exact test, or Pearson's Chi-squared test, as appropriate.

[00107] Abbreviations: left ventricle (LV); beats per minute (BPM); ejection fraction (EF). [00108] TABLE 4: Comparison of ECG-Echo Pair Demographics, ECG Characteristics, and Echo Characteristics Stratified by LV Dysfunction Outcome

[00109] Data presented as median (interquartile range). Age in years. P-values obtained by Wilcoxon rank sum test, Fisher's exact test, or Pearson's Chi-squared test, as appropriate.

[00110] Abbreviations: left ventricle (LV); beats per minute (BPM); ejection fraction (EF).

[00111] TABLE 5 : Comparison of ECG-Echo Pair Demographics, ECG Characteristics, and Echo Characteristics Stratified by LV Hypertrophy Outcome

[00112] Data presented as median (interquartile range). Age in years. P-values obtained by Wilcoxon rank sum test, Fisher's exact test, or Pearson's Chi-squared test, as appropriate.

[00113] Abbreviations: left ventricle (LV); beats per minute (BPM); ejection fraction (EF).

[00114] TABLE 6: Comparison of ECG-Echo Pair Demographics, ECG Characteristics, and Echo Characteristics Stratified by LV Dilation Outcome

[00115] Data presented as median (interquartile range). Age in years. P-values obtained by Wilcoxon rank sum test, Fisher's exact test, or Pearson's Chi-squared test, as appropriate.

[00116] Abbreviations: left ventricle (LV); beats per minute (BPM); ejection fraction (EF).

Example 2: Data Retrieval

[00117] The purpose of this example is to provide an exemplary method for retrieving the ECG data. Raw ECG signals were exported from the MUSE ECG data management system (GE Healthcare, Chicago, IL). Waveform data were obtained from XML files, where each onedimensional vector of data sampled at a rate of 250 Hz for 10 seconds duration (2500 samples) corresponds to a lead (I, II, and V1-V6). Linear transformations of the vectors were performed based on the Einthoven law and Goldberger equation to obtain leads III, aVF, aVL, and aVR. Age, sex, and physician reviewed ECGs measurements (e.g., QRS interval, QRS axis, T axis, P axis, PR interval, QT interval, QTc, and heart rate) are archived in an internal database at Boston Children’s Hospital, which was also retrieved. In addition, ECG-based diagnoses of LV hypertrophy (as coded by expert pediatric electrophysiologists) were retrieved for benchmarking purposes.

[00118] Similarly, echo reports written by pediatric cardiologists are archived in an internal database at Boston Children’s Hospital; extracted records contained the human expert classification of the degree of left ventricular (LV) systolic dysfunction, hypertrophy, and/or dilation (if any). Potential grades were “trivial”, “mild”, “mild-to-moderate”, “moderate”, “moderate-to-severe”, and “severe”. When available, quantitative measures of LV ejection fraction (% and z-score), LV mass (raw and z-score), LV mass/volume (raw and z- score), and LV end- diastolic volume (raw and z-score) were obtained. For both extracted ECG and echo records, unique patient identifiers and dates and times are available to link ECG-echo pairs and filter based on timing of events.

Example 3: Quality Control and Data Preprocessing

[00119] The purpose of this example is to provide an exemplary method of preprocessing the ECG data. In the case of multiple ECG recording attempts for a given ECG event, the final recorded ECG is retrieved. This ECG is then discarded if any lead is not 2500 samples long, or if any lead recording has no lead information (i.e., flat line). Given that ECGs are prone to recording errors (e.g., baseline wander; electrical interference), a high pass filter was utilized. The ECG was then trimmed to 2048 samples (approximately 8 seconds) to facilitate conveniently working with convolution neural networks.

Example 4: Definition of Primary Outcomes

[00120] The purpose of this example is to provide exemplary cardiac conditions that may be used for training the models described herein. Individual outcomes included LV systolic dysfunction, LV hypertrophy, and LV dilation. Human expert knowledge was considered as a ground truth, whereby: 1) LV systolic dysfunction was considered positive if the echo report was coded by a pediatric cardiologist for qualitatively greater than “mild” LV systolic dysfunction; 2) LV hypertrophy was considered positive if the echo report was coded by a pediatric cardiologist for qualitatively greater than “mild” LV hypertrophy, or LV hypertrophic cardiomyopathy; and 3) LV dilation was considered positive if the echo report was coded by a pediatric cardiologist for qualitatively greater than “mild” LV dilation, or LV dilated cardiomyopathy. The composite outcome was defined as having positive LV systolic dysfunction, hypertrophy, or dilation. The primary outcomes were used to train the models used herein.

[00121] As a secondary subgroup performance analysis of the human expert trained model, we implemented quantitative cutoffs for the above outcomes, whereby LV ejection fraction, LV mass, and LV end-diastolic volume z-scores of < -2.5, > 2.5, and > 2.5 (corresponding to quantitative “mild” cutoffs) were considered positive for LV dysfunction, hypertrophy, and dilation, respectively. LV ejection fraction, LV mass, and LV end-diastolic volume z-score cutoffs of < -4.0, > 4.0, and > 4.0 (corresponding to quantitative “moderate” cutoffs), respectively, as well as < -6.0, > 6.0, and > 6.0 (corresponding to quantitative “severe” cutoffs), respectively, were also considered.

Example 5: Model Selection, Architecture, and Training [00122] The purpose of this example is to provide exemplary methods for developing a model described herein. An exemplary model was developed solely on the training set, which was further partitioned 95% for training and 5% for validation to allow for hyperparameter tuning. A convolutional neural network used 12 x 2048 ECG inputs in a convolutional neural network similar to a residual network (i.e., including skip connections) that is adapted for unidimensional signals. [00123] An exemplary artificial intelligence-enhanced pediatric ECG (AI-pECG) network consisted of a convolutional layer followed by four residual blocks with two convolutional layers per block.

[00124] The output of each convolutional layer is rescaled using batch normalization, and fed into a rectified linear activation unit, after which dropout at a rate of 0.8 is applied. Max pooling and convolutional layers with filter length 1 are included in the skip connections to make the dimensions match those from the signals in the main branch. The output of the last block is fed into a fully connected layer with a sigmoid activation function. Demographics (i.e., age and sex) were also incorporated as inputs along with ECG waveforms in a separate deep learning model (AI-pECG + age + sex). More specifically, the above architecture was modified by adding a separate part of the model, where we concatenate demographics (i.e., age and sex) and pass it through a fully connected layer. The outputs for demographic and ECG model parts are individually flattened and then concatenated to obtain one feature vector. The resulting feature vector was fed into the final fully connected layer with a sigmoid activation function.

[00125] For each model, the final configuration hyperparameters were obtained via a grid search on the training set among the following options: kernel size [3, 9, 17], batch size [8, 32, 64], and initial learning rate [0.01, 0.001, 0.0001, 0.00001], The average cross-entropy was minimized using the Adam optimizer. Maximum 150 epochs with early stopping were used based on validation loss. The model with the lowest validation loss during hyperparameter tuning was selected as an exemplary final trained model.

Example 6: Performance Evaluation and Statistical Analyses

[00126] The purpose of this example is to provide exemplary methods for evaluating a model described herein. Model performance was evaluated on the internal and external test groups. Given the nature of an imbalanced dataset, the area under the receiver operating curve (AUROC) as well as the area under the precision-recall (i.e., positive predictive value -sensitivity) curve (AUPRC) were computed. The DeLong test was performed to compare AUROCs across models. To benchmark an LV hypertrophy model, pediatric electrophysiologist expert ECG-based diagnoses of LV hypertrophy (using the first available ECG per patient to minimize human bias from prior echo findings) were used. Other performance metrics evaluated included positive predictive value, negative predictive value, sensitivity, and specificity. For all metrics, a higher value is indicative of better performance. Resampling with 1,000 bootstraps was implemented to obtain performance metric confidence intervals.

[00127] After training an exemplary AI-pECG model on nearly 100,000 ECG-echo pairs with corresponding human expert classified greater than mild LV dysfunction, hypertrophy, and dilation, model performance was tested.

[00128] During internal testing, the exemplary AI-pECG model achieved AUROCs of 0.85 [95% CI, 0.84-0.86], 0.88 [95% CI, 0.86-0.89], 0.85 [95% CI, 0.83-0.86] and 0.86 [95% CI, 0.84- 0.87] for the LV composite outcome, LV dysfunction, LV hypertrophy, and LV dilation, respectively (FIG. 5). Notably, the AI-pECG model outperformed the pediatric electrophysiologist expert ECG-based diagnosis of LV hypertrophy, which had a sensitivity of 33%, specificity of 95%, and positive predictive value of 11.1% (FIG. 5; grey dot). Adding age and sex to the AI- pECG model led to similar performance for the LV composite outcome (p=0.07), LV hypertrophy (p=0.3), and LV dilation (p=0.3), with a minor yet statistically significant difference for LV dysfunction (AUROC 0.86 [95% CI 0.85-0.88]; p<0.01) (FIG. 5). When looking at single random ECGs per patient, model performance remained high (FIG. 6).

[00129] During testing on the external cohort, the AI-ECG model achieved AUROCs of 0.81 [95% CI, 0.78-0.84], 0.83 [95% CI, 0.79-0.87], 0.84 [95% CI, 0.80-0.87] and 0.86 [95% CI, 0.83-0.90] for the LV composite outcome, LV dysfunction, LV hypertrophy, and LV dilation, respectively (FIG. 7). The AI-pECG model again outperformed the pediatric electrophysiologist expert ECG-based diagnosis of LV hypertrophy, which had a sensitivity of 24%, specificity of 95%, and positive predictive value of 13.2% (FIG. 7; grey dot). Adding age and sexto the AI-ECG model led to similar performance for the LV composite outcome (p=0.8), LV dysfunction (p=0.4), LV hypertrophy (p=0.07), and LV dilation (p=0.9). (FIG. 7). When looking at single random ECGs per patient, model performance remained high (FIG. 8).

[00130] Performance of the AI-pECG model, trained on human expert qualitative cutoffs, was next explored to discriminate between quantitative cutoffs for LV dysfunction (LV ejection fraction z-score < -2.5, -4, -6.0), LV hypertrophy (LV mass z-score > +2.5, +4, +6.0), LV dilation (LV end-diastolic volume z-score > +2.5, +4, +6.0), and the composite outcome for each corresponding cutoff (FIG. 9). In general, performance when using a z-score cutoff of 2.5 was comparable to performance using our qualitative cutoffs (FIG. 9). Performance increased with a higher cutoff deviating from normal, with AUROCs of > 0.85 and > 0.9 for every outcome in both internal and external test groups using a z-score cutoff of 4.0 and 6.0, respectively.

Example 7: Subgroup Analyses [00131] The purpose of this example is to provide exemplary subgroup analysis of the methods described herein. Age and sex are known to influence ECG characteristics in a healthy pediatric population, and were therefore explored in subgroup analyses. Age partitioning was adapted with groupings of age < 1, 1 < age < 3, 3 < age < 8, 8 < age < 12, and 12 < age < 18 years. AUROCs were calculated for each subgroup.

AI-pECG performance when stratifying by age and sex was next examined (FIG. 10). In the LV composite, LV dysfunction, and LV hypertrophy groups, performance varied with age, most notably for predicting LV hypertrophy in age < 1 year. The algorithm appeared to have slightly better performance in females compared to males for predicting the LV composite outcome, LV dysfunction, and LV dilation (FIG. 10).

Example 8: Saliency Mapping

[00132] The purpose of this example is to provide exemplary saliency mapping for model interpretation using the methods described herein. In an effort to provide model interpretability, the following analyses were performed to identify which features of the ECG input may contribute to model prediction: 1) median waveform analysis; and 2) saliency mapping. Similar to others, median waveform analysis is a technique for visualizing aggregated ECG samples into a single beat. By doing so, examples of high-risk and low-risk ECGs may be visualized. Herein, the 100 highest predicted ECGs for a given outcome in the internal test set were used to create high-risk median waveforms, and the 100 lowest predicted ECGs in the internal test set were used to create low-risk median waveforms. Median waveforms in each lead were generated by: 1) QRS complex detection; 2) interpolating all ECGs to the same heart rate; 3) computing the median voltage across beats for each patient; 4) computing the median voltage across patients for each time bin in the cardiac cycle. Saliency mapping was helpful in identifying which features of the ECG input contribute to model prediction. Saliency maps highlight components of the ECG where a change in input (i.e., ECG voltage) leads to a change in prediction. Saliency maps were created using a Shapley Additive Explanations (SHAP) framework. To highlight the most influential components of the ECG waveform, the SHAP values for the high-risk ECGs were obtained. Subsequently, the above steps to generate median waveforms were implemented on SHAP values over time. The resultant darker regions in saliency maps correspond to greater contribution to the prediction.

As shown in FIG. 11, to predict LV hypertrophy, the salient features in some cases may be or include precordial QRS complexes. High-risk features to predict LV hypertrophy include deep S waves in V1-V2. In limb lead I, the QRS complex was also salient, with high-risk features including a high amplitude R wave. For LV dilation, the salient features in some cases may be or include lateral precordial (V4-V6) QRS complexes. High-risk features to predict LV dilation include high amplitude R waves in V4-V6. As shown in FIG. 12, the saliency map for the composite outcome appears to merge features from each of the individual outcomes of interest.

[00133] Embodiments have been described where the techniques are implemented in circuitry and/or computer-executable instructions. It should be appreciated that some embodiments may be in the form of a method, of which at least one example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

[00134] Various aspects of the embodiments described above may be used alone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing and is therefore not limited in its application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.

[00135] Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.

[00136] Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” “having,” “containing,” “involving,” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

[00137] The word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any embodiment, implementation, process, feature, etc. described herein as exemplary should therefore be understood to be an illustrative example and should not be understood to be a preferred or advantageous example unless otherwise indicated.

[00138] Having thus described several aspects of at least one embodiment, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the spirit and scope of the principles described herein. Accordingly, the foregoing description and drawings are by way of example only.

Claims

CLAIMS What is claimed is:

1. A method of predicting a cardiac condition in a pediatric or adult congenital patient, the method comprising: determining, based on received electrocardiogram (ECG) data of a pediatric or adult congenital patient, one or more of a plurality of cardiac phenotypes to which to assign the pediatric or adult congenital patient, at least one cardiac phenotype of the plurality of cardiac phenotypes each corresponding to presence of a cardiac condition, the determining the one or more of the plurality of cardiac phenotypes to assign to the pediatric or adult congenital patient comprising analyzing the received ECG data using one or more trained models, wherein the one or more trained models were trained with training data comprising ECG data from a plurality of prior pediatric or adult congenital patients and information indicating whether each prior patient of the plurality of prior pediatric or adult congenital patients had one or more cardiac conditions; and outputting the one or more of the plurality of cardiac phenotypes determined for the pediatric or adult congenital patient based on the ECG data.

2. The method of claim 1, wherein determining the one or more of a plurality of cardiac phenotypes to which to assign the pediatric or adult congenital patient comprises determining a probability of the pediatric or adult congenital patient having one or more cardiac conditions.

3. The method of claim 1, wherein: determining a probability of the pediatric or adult congenital patient having one or more cardiac conditions comprises determining a probability of the pediatric or adult congenital patient having any of the one or more cardiac conditions; and determining the one or more of the plurality of cardiac phenotypes to which to assign the pediatric or adult congenital patient comprises, based on the probability of the pediatric or adult congenital patient having any of the one or more cardiac conditions, determining whether to assign the pediatric or adult congenital patient to a phenotype for pediatric or adult congenital patients with any of the one or more cardiac conditions.

4. The method of claim 2, wherein determining a probability of the pediatric or adult congenital patient having one or more cardiac conditions comprises determining a probability of the pediatric or adult congenital patient having each of the one or more cardiac conditions, wherein the one or more cardiac conditions comprise mortality, ventricular dysfunction, ventricular dilation, and ventricular hypertrophy.

5. The method of claim 4, wherein determining the one or more of the plurality of cardiac phenotypes comprises comparing the probability of the pediatric or adult congenital patient having each of the one or more cardiac conditions to a threshold.

6. The method of claim 1, wherein determining one or more of the plurality of cardiac phenotypes comprises determining one or more of a mortality risk, ventricular dysfunction phenotype, ventricular dilation phenotype, and ventricular hypertrophy phenotype.

7. The method of claim 1, wherein determining one or more of the plurality of cardiac phenotypes comprises determining whether the pediatric or adult congenital patient has a ventricular dysfunction.

8. The method of claim 1, wherein determining one or more of the plurality of cardiac phenotypes comprises determining whether the pediatric or adult congenital patient has ventricular dilation.

9. The method of claim 1, wherein determining one or more of the plurality of cardiac phenotypes comprises determining whether the pediatric or adult congenital patient has ventricular hypertrophy.

10. The method of claim 1, wherein determining one or more of the plurality of cardiac phenotypes comprises determining whether the pediatric or adult congenital patient has a risk of mortality.

11. The method of claim 1, wherein determining one or more of the plurality of cardiac phenotypes further comprises assigning a qualitative assessment for the one or more of the plurality of cardiac phenotypes, wherein the qualitative assessment indicates whether a severity of a cardiac condition indicated by a cardiac phenotype.

12. The method of claim 1, wherein determining the one or more of the plurality of cardiac phenotypes based on the received ECG data comprises determining the one or more of the plurality of cardiac phenotypes based on one or more of: at least one ECG raw waveform, a QRS interval, a QRS axis, a T axis, a P axis, a PR interval, a QT interval, a QT corrected for heart rate (QTc), or a heart rate.

13. The method of claim 1, wherein determining using the one or more trained models comprises determining using the one or more trained models were trained with training data that did not include data for pediatric patients having congenital heart disease.

14. The method of claim 1, wherein determining using the one or more trained models comprises determining using the one or more trained models were trained with training data including data for pediatric or adult patients having congenital heart disease.

15. The method of claim 1, wherein determining using the one or more trained models comprises determining using one or more trained models were trained with training data for patients across a plurality of age ranges, each of the plurality of age ranges being a range within an overall age range of 0 to 18 years, and greater than 18 years for adult congenital heart disease.

16. The method of claim 13, wherein the plurality of age ranges comprise two or more of: 0 years to 1 year, 1 year to 3 years, 3 years to 8 years, 8 years to 12 years, 12 years to 18 years, or greater than 18 years in the case of adult congenital heart disease.

17. The method of claim 13, wherein the plurality of age ranges comprise two or more of: infancy, toddlerhood, school age, preadolescence, adolescence, and adults.

18. The method of claim 1, wherein determining using the one or more trained models comprises determining using one or more trained models were trained with training data comprising age data and/or sex data from the plurality of prior pediatric or adult congenital patients, wherein each of the age data and/or the sex data is combined in the training with the ECG data from the plurality of prior pediatric or adult congenital patients.

19. The method of claim 1, wherein determining using the one or more trained models comprises determining using one or more trained models were trained with training data comprising echocardiogram data from the plurality of prior pediatric or adult congenital patients, wherein the echocardiogram data is combined with the ECG data from the plurality of prior pediatric or adult congenital patients.

20. The method of claim 17, wherein the echocardiogram data of the training data comprises one or more of a death date, ventricular ejection fraction EF, a ventricular mass, a ventricular mass/volume, and a ventricular end-diastolic volume.

21. The method of claim 1, wherein determining using one or more trained models trained with ECG data from the plurality of prior pediatric or adult congenital patients comprises determining using one or more trained models trained with filtered ECG data for the plurality of prior pediatric or adult congenital patients, the filtered ECG data comprising noise exceeding a threshold.

22. A method of predicting a cardiac condition in a pediatric or adult congenital patient, the method comprising: determining, based on received ECG data from a pediatric or adult congenital patient, a prediction of whether the pediatric or adult congenital patient has any of one or more cardiac conditions, the determining the prediction comprising analyzing the received ECG data using one or more trained models, wherein the one or more trained models have been trained with ECG data from a plurality of prior pediatric or adult congenital patients; and outputting the prediction of whether the pediatric or adult congenital patient has any of the one or more cardiac conditions.

23. A method of predicting cardiac conditions in pediatric or adult congenital patients, the method comprising: determining, based on first received ECG data from a first pediatric or adult congenital patient, a first prediction of whether the first pediatric or adult congenital patient has one or more cardiac conditions, the determining comprising analyzing the first received ECG data using a trained model; determining, based on second received ECG data from a second pediatric or adult congenital patient, a second prediction of whether the second pediatric or adult congenital patient has the one or more cardiac conditions, the determining comprising analyzing the second received ECG data using the trained model; and outputting the first prediction and the second prediction for the first pediatric or adult congenital patient and the second pediatric or adult congenital patient, wherein the trained model was trained with training data comprising ECG data and cardiac condition data from a plurality of prior pediatric or adult congenital patients comprising patients from multiple age ranges of pediatric or adult congenital patients, the multiple age ranges comprising at least one adolescent age range and at least one preadolescent age range, and wherein the first pediatric or adult congenital patient has an age in the at least one adolescent age range and the second pediatric or adult congenital patient has an age in the at least one preadolescent age range.

24. A system for predicting a cardiac condition in a pediatric or adult congenital patient, the system comprising: an ECG monitoring device; a controller comprising at least one processor, at least one storage medium having encoded thereon executable instructions that, when executed by the at least one processor, cause the at least one processor to carry out a method comprising: determining, based on received ECG data from a pediatric or adult congenital patient, information regarding whether the pediatric or adult congenital patient has one or more cardiac conditions, the determining comprising analyzing the received ECG data using one or more trained model, and wherein the one or more trained model has been trained with training data comprising ECG data from a plurality of prior pediatric or adult congenital patients; and outputting the information regarding whether the pediatric or adult congenital patient has the one or more cardiac conditions.

25. At least one storage medium having encoded thereon executable instructions that, when executed by at least one processor, cause the at least one processor to carry out a method comprising: determining, based on received ECG data from a pediatric or adult congenital patient, information regarding whether the pediatric or adult congenital patient has one or more cardiac conditions, the determining comprising analyzing the received ECG data using one or more trained model, and wherein the one or more trained model has been trained with training data comprising ECG data from a plurality of prior pediatric or adult congenital patients; and outputting the information regarding whether the pediatric or adult congenital patient has the one or more cardiac conditions.