CN115089112A - Method and device for establishing post-stroke cognitive disorder risk assessment model and electronic equipment - Google Patents
Method and device for establishing post-stroke cognitive disorder risk assessment model and electronic equipment Download PDFInfo
- Publication number
- CN115089112A CN115089112A CN202210487286.5A CN202210487286A CN115089112A CN 115089112 A CN115089112 A CN 115089112A CN 202210487286 A CN202210487286 A CN 202210487286A CN 115089112 A CN115089112 A CN 115089112A
- Authority
- CN
- China
- Prior art keywords
- stroke
- post
- data set
- model
- dysfunction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 208000010877 cognitive disease Diseases 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000012502 risk assessment Methods 0.000 title claims abstract description 49
- 230000004064 dysfunction Effects 0.000 claims abstract description 54
- 208000028698 Cognitive impairment Diseases 0.000 claims abstract description 51
- 230000033001 locomotion Effects 0.000 claims abstract description 44
- 238000013528 artificial neural network Methods 0.000 claims abstract description 30
- 230000011218 segmentation Effects 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims abstract description 19
- 238000012549 training Methods 0.000 claims abstract description 19
- 230000015654 memory Effects 0.000 claims description 23
- 230000006870 function Effects 0.000 claims description 20
- 238000003745 diagnosis Methods 0.000 claims description 15
- 206010061296 Motor dysfunction Diseases 0.000 claims description 14
- 238000005516 engineering process Methods 0.000 claims description 14
- 206010044565 Tremor Diseases 0.000 claims description 12
- 238000013527 convolutional neural network Methods 0.000 claims description 11
- 238000010183 spectrum analysis Methods 0.000 claims description 10
- 238000013210 evaluation model Methods 0.000 claims description 9
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000003058 natural language processing Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 7
- 230000009466 transformation Effects 0.000 claims description 6
- 208000012661 Dyskinesia Diseases 0.000 claims description 5
- 230000003902 lesion Effects 0.000 claims description 2
- 208000006011 Stroke Diseases 0.000 description 122
- 238000012216 screening Methods 0.000 description 9
- 238000004891 communication Methods 0.000 description 8
- 238000011156 evaluation Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 4
- 230000000306 recurrent effect Effects 0.000 description 4
- 206010008190 Cerebrovascular accident Diseases 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000002490 cerebral effect Effects 0.000 description 3
- 238000003759 clinical diagnosis Methods 0.000 description 3
- 238000002591 computed tomography Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000012098 association analyses Methods 0.000 description 2
- 230000002457 bidirectional effect Effects 0.000 description 2
- 210000004556 brain Anatomy 0.000 description 2
- 238000010968 computed tomography angiography Methods 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 2
- 125000004122 cyclic group Chemical group 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000003709 image segmentation Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000004083 survival effect Effects 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 208000032843 Hemorrhage Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000003920 cognitive function Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000006735 deficit Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000000556 factor analysis Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000000302 ischemic effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000006403 short-term memory Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
- A61B5/4088—Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
- A61B5/1101—Detecting tremor
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
- A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb using a particular sensing technique
- A61B5/1128—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb using a particular sensing technique using image analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4803—Speech analysis specially adapted for diagnostic purposes
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Surgery (AREA)
- Molecular Biology (AREA)
- Medical Informatics (AREA)
- Animal Behavior & Ethology (AREA)
- Heart & Thoracic Surgery (AREA)
- Public Health (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Veterinary Medicine (AREA)
- Biomedical Technology (AREA)
- Physiology (AREA)
- Artificial Intelligence (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Neurology (AREA)
- Dentistry (AREA)
- Psychiatry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Child & Adolescent Psychology (AREA)
- Neurosurgery (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Developmental Disabilities (AREA)
- Signal Processing (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Hospice & Palliative Care (AREA)
- Psychology (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The application relates to a method and a device for establishing a post-stroke cognitive disorder risk assessment model and electronic equipment, wherein the method comprises the following steps: the method comprises the steps of obtaining a multi-modal relevant data set of a target population, inputting the data set into a pre-constructed stroke segmentation model to obtain the size and position characteristics of a stroke focus of the target population, analyzing a clinical report data set based on a pre-constructed post-stroke cognitive impairment PSCI relevance analysis model to obtain the PSCI risk probability of the target population, inputting a voice data set into a pre-constructed post-stroke language dysfunction assessment model to obtain the language dysfunction rating of the target population, inputting a video data set into a post-stroke motion dysfunction assessment model to obtain the motion dysfunction rating of the target population, training a prediction model formed by a multilayer neural network based on an obtained relevant rating result, and obtaining a post-stroke cognitive impairment risk assessment model. Thus, post-stroke cognitive impairment is more accurately assessed.
Description
Technical Field
The application relates to the technical field of risk assessment, in particular to a method and a device for establishing a post-stroke cognitive disorder risk assessment model and electronic equipment.
Background
The cerebral apoplexy is the leading cause of death and disability of urban residents, and part of stroke patients can continuously experience Post-stroke Cognitive Impairment (PSCI), so that the life quality and the survival time of the patients are seriously affected, and the cerebral apoplexy is one of the important causes of serious burden of cerebral apoplexy diseases in China.
In the related art, prevention and treatment of PSCI are mainly achieved through early evaluation and intervention so as to prevent the occurrence of PSCI and delay the development of cognitive disorder. The PSCI diagnosis and evaluation method widely used at present mainly utilizes a screening scale, a doctor scores each function of a patient by requiring the patient to perform a series of demonstration according to the screening scale, and the cognitive impairment development condition of the patient is comprehensively evaluated by combining the stroke clinical diagnosis result.
However, it is a very time consuming task to manually analyze clinical diagnosis results of stroke patients and calculate related diagnosis indexes, there are contradictions between short time consumption and complete coverage when the PSCI is evaluated by a screening scale, and the evaluation results have strong subjective dependence on doctors, and are prone to generate large errors, so that a solution is urgently needed.
Disclosure of Invention
The application provides a method and a device for establishing a post-stroke cognitive impairment risk assessment model, electronic equipment and a storage medium, and aims to solve the problems that in the related art, PSCI is assessed through a screening scale, the contradiction of short time consumption and complete coverage exists, the subjective dependency of assessment results on doctors is strong, large errors are prone to being generated, and the like.
The embodiment of the first aspect of the application provides a method for establishing a post-stroke cognitive impairment risk assessment model, which comprises the following steps:
acquiring an image data set, a clinical report data set, a voice data set and a video data set of multiple modes of a target population;
inputting the multi-modal image data sets into a pre-constructed stroke segmentation model to obtain the size and position characteristics of stroke lesions of the target population, and analyzing the clinical report data sets based on a pre-constructed post-stroke cognitive impairment (PSCI) relevance analysis model to obtain PSCI risk probability of the target population;
inputting the voice data set into a pre-constructed post-stroke language dysfunction evaluation model to obtain a language dysfunction rating of the target population, and inputting the video data set into a post-stroke movement dysfunction evaluation model to obtain a movement dysfunction rating of the target population; and
and training a prediction model formed by a multilayer neural network based on the size and position characteristics of the stroke focus, the PSCI risk probability, the language dysfunction rating and the motor dysfunction rating to obtain a post-stroke cognitive disorder risk assessment model.
Optionally, before inputting the image data sets of the multiple modalities into the pre-constructed stroke segmentation model, the method further includes:
registering the image data sets of the multiple modes by using a medical image registration method to obtain registered image data sets of the multiple modes;
and training a preset convolutional neural network based on the registered multi-modal image data set and the corresponding diagnosis result, and generating the stroke segmentation model.
Optionally, before analyzing the clinical report data set based on the post-stroke cognitive impairment PSCI correlation analysis model, further comprising:
learning, from the clinical report dataset, a demographic characteristic, a correlation of a clinical factor with post-stroke cognitive impairment using a natural language processing algorithm;
and constructing the PSCI relevance analysis model according to the relevance.
Optionally, before inputting the speech data set to the post-stroke language dysfunction assessment model, further comprising:
extracting features related to language functions from the speech dataset;
and training a circulating neural network based on the features related to the language function, and constructing the post-stroke language dysfunction assessment model.
Optionally, before inputting the video data set to the post-stroke motor dysfunction assessment model, further comprising:
extracting human body joint motion from the video data set by using a neural network and a spectrum analysis technology;
calculating an envelope surface of joint point coordinates to obtain human motion amplitude based on the human joint motion, and fitting the human motion amplitude by using wavelet transformation to obtain the frequency of joint tremor;
and constructing the post-stroke movement dysfunction assessment model based on the human body movement amplitude and the frequency of the joint tremor.
The embodiment of the second aspect of the present application provides a post-stroke cognitive impairment risk assessment model establishing device, including:
the acquisition module is used for acquiring an image data set, a clinical report data set, a voice data set and a video data set of multiple modes of a target population;
the risk analysis module is used for inputting the image data sets of the multiple modes into a pre-constructed stroke segmentation model to obtain the size and position characteristics of a stroke focus of the target population, and analyzing the clinical report data set based on a pre-constructed post-stroke cognitive impairment (PSCI) relevance analysis model to obtain a PSCI risk probability of the target population;
the grading module is used for inputting the voice data set into a pre-constructed post-stroke language dysfunction evaluation model to obtain the language dysfunction grade of the target population, and inputting the video data set into a post-stroke movement dysfunction evaluation model to obtain the movement dysfunction grade of the target population; and
and the generation module is used for training a prediction model formed by a multilayer neural network based on the size and position characteristics of the stroke focus, the PSCI risk probability, the language dysfunction rating and the motor dysfunction rating to obtain a post-stroke cognitive disorder risk assessment model.
Optionally, before inputting the image datasets of the multiple modalities into a pre-constructed stroke segmentation model, the risk analysis module is specifically configured to:
registering the image data sets of the multiple modes by using a medical image registration method to obtain registered image data sets of the multiple modes;
and training a preset convolutional neural network based on the registered multi-modal image data set and the corresponding diagnosis result, and generating the stroke segmentation model.
Optionally, before analyzing the clinical report dataset based on the post-stroke cognitive impairment PSCI correlation analysis model, the risk analysis module is further configured to:
learning from the clinical report dataset demographic characteristics, associations of clinical factors with post-stroke cognitive impairment using a natural language processing algorithm;
and constructing the PSCI relevance analysis model according to the relevance.
Optionally, before inputting the speech data set to the post-stroke language dysfunction assessment model, the rating module is specifically configured to:
extracting features related to language functions from the speech dataset;
and training a circulating neural network based on the features related to the language function, and constructing the post-stroke language dysfunction assessment model.
Optionally, before inputting the video data set to the post-stroke motor dysfunction assessment model, the rating module is further configured to:
extracting human body joint motion from the video data set by using a neural network and a spectrum analysis technology;
calculating an envelope surface of joint point coordinates to obtain human motion amplitude based on the human joint motion, and fitting the human motion amplitude by using wavelet transformation to obtain the frequency of joint tremor;
and constructing the post-stroke dyskinesia assessment model based on the human body motion amplitude and the frequency of the joint tremor.
An embodiment of a third aspect of the present application provides an electronic device, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the method for establishing the post-stroke cognitive disorder risk assessment model according to the embodiment.
A fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, where the program is executed by a processor, so as to implement the method for establishing a post-stroke cognitive impairment risk assessment model according to the foregoing embodiment.
Therefore, the method for establishing the post-stroke cognitive disorder risk assessment model has the following advantages.
(1) The embodiment of the application utilizes data of various different modes, and different data can play a role in mutual complementation and mutual promotion.
(2) According to the method and the device, richer feature expressions can be extracted by using data of various different modes, and the accuracy and the robustness of the model are improved.
(3) According to the embodiment of the application, different modal data and multiple neural network technologies are utilized, the cognitive disorder grading and risk prediction after stroke are automatically realized, and the subjectivity of manual diagnosis is avoided.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a method for establishing a post-stroke cognitive impairment risk assessment model according to an embodiment of the present application;
fig. 2 is a block diagram illustrating an apparatus of a post-stroke cognitive impairment risk assessment model establishment method according to an embodiment of the present application;
fig. 3 is an exemplary diagram of an electronic device provided according to an embodiment of the application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The following describes a method, an apparatus, an electronic device, and a storage medium for establishing a post-stroke cognitive impairment risk assessment model according to embodiments of the present application with reference to the accompanying drawings. Aiming at the problems that in the related technology mentioned in the background technology center, PSCI is evaluated through a screening scale, the time consumption is short, the coverage is complete, the subjective dependence of an evaluation result on doctors is strong, and large errors are easy to generate, the application provides a method for establishing a post-stroke cognitive impairment risk evaluation model.
Before introducing the method for establishing the post-stroke cognitive impairment risk assessment model according to the embodiment of the application, the post-stroke cognitive impairment and the related technology are introduced.
The post-stroke cognitive impairment (PSCI) is a persistent cognitive function impairment closely related to stroke pathological characteristics, has the characteristic of high morbidity, and about 30 to 80 percent of stroke patients can continuously experience the post-stroke cognitive impairment within 3 to 6 months after the stroke occurs, so that the life quality and the survival time of the patients are seriously influenced.
Various medical image aids including CT (Computed Tomography), MR (Magnetic Resonance examination), and the like are not known to be available for diagnosing stroke and calculating quantitative indicators related to stroke (e.g., ischemic volume, hemorrhage volume, and the like). Most of the evaluation contents in the screening scale need to record and analyze the voice and the behavior of the patient, so if the images, clinical reports, voice records and video records of the patient can be simultaneously utilized, the data of the plurality of modes can be comprehensively analyzed by an automatic method, and an automatic post-stroke cognitive impairment risk evaluation system based on multimode data is developed, powerful assistance can be provided for the modern stroke diagnosis and treatment process.
In addition, the deep learning technique has been rapidly developed in recent years, and has been widely used in various fields. The natural language processing method based on the Transformer can effectively analyze large-scale text data, and is suitable for analyzing clinical text data and extracting key information; the medical image processing method based on the convolutional neural network can be used for efficiently extracting features of brain neural images and assisting in stroke diagnosis and calculation of related quantitative indexes; the speech recognition technology and the spectrum analysis method based on the recurrent neural network can accurately analyze and extract key information in speech and video data and accurately evaluate various functions of the patient, such as language, motion and the like. Therefore, by integrating and fusing the technologies and adopting a multi-task deep learning strategy, a post-stroke cognitive disorder risk assessment model based on multi-mode data can be constructed.
Specifically, fig. 1 is a schematic flow chart of a method for establishing a post-stroke cognitive impairment risk assessment model according to an embodiment of the present application.
As shown in fig. 1, the method for establishing the post-stroke cognitive impairment risk assessment model comprises the following steps:
in step S101, image, clinical report, voice, and video datasets of multiple modalities of a target population are acquired.
Specifically, the present example was trained using image datasets, clinical report datasets, speech datasets containing speech dysfunction, and video datasets containing motor dysfunction of 3 different modalities (CT, MR, CTA (CT angiography of arterial vessels)).
In step S102, the image data sets of multiple modalities are input into the pre-constructed stroke segmentation model to obtain the size and position characteristics of the stroke focus of the target population, and the clinical report data set is analyzed based on the pre-constructed post-stroke cognitive impairment PSCI relevance analysis model to obtain the PSCI risk probability of the target population.
Optionally, in some embodiments, before inputting the image data sets of the multiple modalities into the pre-constructed stroke segmentation model, the method further includes: registering the image data sets of multiple modes by using a medical image registration method to obtain registered image data sets of multiple modes; and training a preset convolutional neural network based on the registered image data sets in the multiple modes and the corresponding diagnosis results to generate a stroke segmentation model.
The stroke segmentation model can be constructed based on a large number of medical image learning stroke related iconography characteristics of different modalities by using a convolutional neural network. Optionally, in some embodiments, before analyzing the clinical report data set based on the post-stroke cognitive impairment PSCI association analysis model, further comprising: learning the relevance of demographic characteristics, clinical factors and post-stroke cognitive impairment from the clinical report data set by using a natural language processing algorithm; and constructing a PSCI relevance analysis model according to the relevance.
The PSCI relevance analysis model can learn the relevance of demographic characteristics, clinical factors and cognitive impairment after stroke from a large number of clinical diagnosis reports by using a BERT (preliminary language Representation model) algorithm, and is constructed based on clinical factor analysis.
Specifically, the embodiment of the present application needs to use a medical image registration method to align the input medical images of different modalities to the template, so as to obtain the segmentation results of different brain regions. After the image data of all the data sets are registered, the image data and the corresponding diagnosis results are used together for training an image segmentation model formed by a convolutional neural network, and the size and position characteristics of the stroke focus are obtained. The image segmentation model comprises an encoder and a decoder module, wherein the encoder comprises 3 paths which respectively correspond to 3 different image modalities and map images to low-dimensional image features in the same space; the decoder decodes the low-dimensional image features into the original image. In order to map medical images of different modalities to the same low-dimensional feature space, a counterstudy strategy is used, and a discriminator is arranged to judge which modality the low-dimensional features belong to, so that an encoder is prompted to encode images of different modalities to the same feature space.
In the embodiment of the application, a natural language processing method is further required to be used for training a PSCI relevance analysis model by using text data in a clinical report data set, and characteristics of high relevance between the report and the PSCI are obtained. The PSCI relevance analysis model comprises an encoder module consisting of a bidirectional Transformer structure and a classifier consisting of a multilayer neural network, wherein the encoder module divides input text data into a group of word vectors, and maps the word vectors into a high-dimensional feature space to generate a deep bidirectional language representation capable of fusing left and right context information in the text. And the classifier takes the generated language representation as input in a supervised learning mode, learns the mapping relation between the representation and the PSCI grading result and finally obtains the PSCI risk probability predicted according to the text data.
In step S103, the voice data set is input to a pre-constructed post-stroke language dysfunction assessment model to obtain a language dysfunction rating of the target population, and the video data set is input to a post-stroke movement dysfunction assessment model to obtain a movement dysfunction rating of the target population. Optionally, in some embodiments, before inputting the speech data set to the post-stroke language dysfunction assessment model, further comprising: extracting features related to language functions from the speech dataset; and training the circulating neural network based on the characteristics related to the language function, and constructing a post-stroke language dysfunction assessment model.
The post-stroke language dysfunction assessment model can be constructed by learning language function related features from voice recordings of a large number of stroke patients by using an LSTM (Long Short-Term Memory, time-cycle neural network) algorithm.
Optionally, in some embodiments, before inputting the video data set to the post-stroke motor dysfunction assessment model, further comprising: extracting human body joint motion from the video data set by using a neural network and a spectrum analysis technology; calculating an envelope surface of joint point coordinates to obtain human motion amplitude based on human joint motion, and fitting the human motion amplitude by using wavelet transformation to obtain joint tremor frequency; and constructing a post-stroke dyskinesia assessment model based on the human body motion amplitude and the frequency of joint tremor.
The post-stroke dyskinesia assessment model can be realized by extracting relevant indexes in a PSCI screening scale from a video of a patient through a spectrum analysis technology. Specifically, the embodiment of the application trains a speech feature analysis and language dysfunction assessment model formed by a recurrent neural network by using a language dysfunction data set, inputs a section of audio data recorded by normal persons or language dysfunction patients, performs feature extraction by using the recurrent neural network, maps a section of audio data into an abstract feature space, and further learns a mapping relation between abstract feature representation and whether the speech dysfunction is suffered or not by using the neural network to obtain a post-stroke language dysfunction assessment result generated by audio data analysis.
The embodiment of the application also utilizes a neural network and a spectrum analysis technology, uses a movement dysfunction data set to design a human posture estimation model, extracts human joint movement, then calculates an envelope surface of joint point coordinates to obtain human movement amplitude, and uses wavelet transformation to fit a waveform to obtain the frequency of joint tremor. Finally, the patient's motor dysfunction is automatically rated by reference scales according to tremor amplitude and frequency.
In step S104, a prediction model composed of a multilayer neural network is trained based on the size and position characteristics of the stroke focus, the PSCI risk probability, the language dysfunction rating, and the motor dysfunction rating, to obtain a post-stroke cognitive impairment risk assessment model.
The post-stroke cognitive disorder risk assessment model can utilize the multilayer feature extraction capability of the neural network to extract complete post-stroke cognitive disorder feature representation according to the acquired multiple features and indexes to achieve accurate construction.
Specifically, the embodiment of the application integrates the multitask results and is used for training a PSCI prediction model formed by a multilayer neural network. The PSCI prediction model inputs the adopted stroke position and size characteristics, the PSCI risk probability based on clinical report prediction, the language dysfunction rating based on audio data generation and the motor dysfunction rating based on video data generation, and learns the mapping relation between the multiple indexes and the final PSCI diagnosis rating by utilizing the multi-layer characteristic extraction capability of the neural network to obtain the final post-stroke cognitive impairment risk assessment model based on multi-mode data.
According to the method for establishing the post-stroke cognitive disorder risk assessment model, the patient demographic characteristics, the stroke related characteristics and the PSCI related risk factors are extracted through a Transformer, diagnosis of stroke and calculation of stroke quantitative indexes are achieved through a convolutional neural network, the language function of a patient is assessed through a cyclic neural network, abnormal actions in a video of the patient are identified and quantified through spectrum analysis, and accurate cognitive disorder rating is achieved through a multi-task learning strategy. Therefore, the problems that in the related technology, PSCI is evaluated through a screening scale, the contradiction of short time consumption and complete coverage exists, the subjective dependence of an evaluation result on a doctor is strong, large errors are easy to generate and the like are solved.
Next, a post-stroke cognitive impairment risk assessment model establishment device according to an embodiment of the present application will be described with reference to the drawings.
Fig. 2 is a block diagram of a post-stroke cognitive impairment risk assessment model building apparatus according to an embodiment of the present application.
As shown in fig. 2, the post-stroke cognitive impairment risk assessment model creation apparatus 10 includes: an acquisition module 100, a risk analysis module 200, a rating module 300, and a generation module 400.
The acquisition module 100 is configured to acquire an image data set, a clinical report data set, a voice data set, and a video data set of multiple modalities of a target population;
the risk analysis module 200 is used for inputting the image data sets of multiple modes into a pre-constructed stroke segmentation model to obtain the size and position characteristics of a stroke focus of a target population, and analyzing the clinical report data set based on the pre-constructed post-stroke cognitive impairment PSCI relevance analysis model to obtain the PSCI risk probability of the target population;
the rating module 300 is configured to input the voice data set to a pre-constructed post-stroke language dysfunction assessment model to obtain a language dysfunction rating of the target population, and input the video data set to a post-stroke movement dysfunction assessment model to obtain a movement dysfunction rating of the target population; and
the generation module 400 is configured to train a prediction model formed by a multilayer neural network based on the size and position characteristics of the stroke focus, the PSCI risk probability, the language dysfunction rating, and the motor dysfunction rating, so as to obtain a post-stroke cognitive impairment risk assessment model.
Optionally, in some embodiments, before inputting the image data sets of the multiple modalities into the pre-constructed stroke segmentation model, the ranking module 300 is specifically configured to:
registering the image data sets of multiple modes by using a medical image registration method to obtain registered image data sets of multiple modes;
and training a preset convolutional neural network based on the registered image data sets in the multiple modes and the corresponding diagnosis results to generate a stroke segmentation model.
Optionally, in some embodiments, before analyzing the clinical report dataset based on the post-stroke cognitive impairment PSCI association analysis model, the risk analysis module 200 is specifically configured to:
learning the relevance of demographic characteristics, clinical factors and post-stroke cognitive impairment from a clinical report data set by using a natural language processing algorithm;
and constructing a PSCI relevance analysis model according to the relevance.
Optionally, in some embodiments, prior to inputting the speech data set to the post-stroke language dysfunction assessment model, the rating module 300 is specifically configured to:
extracting features related to language functions from the speech dataset;
and training the recurrent neural network based on the characteristics related to the language function, and constructing a post-stroke language dysfunction assessment model.
Optionally, in some embodiments, before inputting the video data set to the post-stroke motor dysfunction assessment model, the rating module 300 is further configured to:
extracting human body joint motion from the video data set by using a neural network and a spectrum analysis technology;
calculating an envelope surface of joint point coordinates to obtain human motion amplitude based on human joint motion, and fitting the human motion amplitude by using wavelet transformation to obtain joint tremor frequency;
and constructing a post-stroke dyskinesia assessment model based on the human body motion amplitude and the frequency of joint tremor.
It should be noted that the explanation of the embodiment of the method for establishing a post-stroke cognitive impairment risk assessment model is also applicable to the device for establishing a post-stroke cognitive impairment risk assessment model of the embodiment, and is not repeated herein.
According to the device for establishing the post-stroke cognitive disorder risk assessment model, the patient demographic characteristics, the stroke related characteristics and the PSCI related risk factors are extracted by using a Transformer, the diagnosis of stroke and the calculation of stroke quantitative indexes are realized by using a convolutional neural network, the language function of the patient is assessed by using the cyclic neural network, the abnormal action in the video of the patient is identified and quantified by using spectral analysis, and finally the accurate rating of the cognitive disorder is realized by using a multi-task learning strategy. Therefore, the problems that in the related technology, PSCI is evaluated through a screening scale, the contradiction of short time consumption and complete coverage exists, the subjective dependence of an evaluation result on a doctor is strong, large errors are easy to generate and the like are solved.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
a memory 301, a processor 302, and a computer program stored on the memory 301 and executable on the processor 302.
The processor 302, when executing the program, implements the method for establishing the post-stroke cognitive impairment risk assessment model provided in the above embodiments.
Further, the electronic device further includes:
a communication interface 303 for communication between the memory 301 and the processor 302.
A memory 301 for storing computer programs operable on the processor 302.
The memory 301 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 301, the processor 302 and the communication interface 303 are implemented independently, the communication interface 303, the memory 301 and the processor 302 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 3, but it is not intended that there be only one bus or one type of bus.
Optionally, in a specific implementation, if the memory 301, the processor 302, and the communication interface 303 are integrated on a chip, the memory 301, the processor 302, and the communication interface 303 may complete communication with each other through an internal interface.
The processor 302 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the program, when executed by a processor, implements the method for establishing the post-stroke cognitive impairment risk assessment model as described above.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, for example, as a sequential list of executable instructions that may be thought of as implementing logical functions, may be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that may fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A method for establishing a post-stroke cognitive disorder risk assessment model is characterized by comprising the following steps:
acquiring an image data set, a clinical report data set, a voice data set and a video data set of multiple modes of a target population;
inputting the multi-modal image data sets into a pre-constructed stroke segmentation model to obtain the size and position characteristics of stroke lesions of the target population, and analyzing the clinical report data sets based on a pre-constructed post-stroke cognitive impairment (PSCI) relevance analysis model to obtain PSCI risk probability of the target population;
inputting the voice data set into a pre-constructed post-stroke language dysfunction evaluation model to obtain the language dysfunction rating of the target population, and inputting the video data set into a post-stroke movement dysfunction evaluation model to obtain the movement dysfunction rating of the target population; and
training a prediction model formed by a multilayer neural network based on the size and position characteristics of the stroke focus, the PSCI risk probability, the language dysfunction rating and the motor dysfunction rating to obtain a post-stroke cognitive disorder risk assessment model.
2. The method of claim 1, further comprising, prior to inputting the multi-modal image data sets to a pre-constructed stroke segmentation model:
registering the image data sets of the multiple modes by using a medical image registration method to obtain registered image data sets of the multiple modes;
and training a preset convolutional neural network based on the registered multi-modal image data set and the corresponding diagnosis result, and generating the stroke segmentation model.
3. The method of claim 2, further comprising, prior to analyzing the clinical report dataset based on the post-stroke cognitive impairment (PSCI) relevance analysis model:
learning, from the clinical report dataset, a demographic characteristic, a correlation of a clinical factor with post-stroke cognitive impairment using a natural language processing algorithm;
and constructing the PSCI relevance analysis model according to the relevance.
4. The method of claim 3, further comprising, prior to inputting the speech data set to the post-stroke language dysfunction assessment model:
extracting features related to language functions from the speech dataset;
and training a circulating neural network based on the features related to the language function, and constructing the post-stroke language dysfunction assessment model.
5. The method of claim 4, further comprising, prior to inputting the video data set to the post-stroke motor dysfunction assessment model:
extracting human body joint motion from the video data set by using a neural network and a spectrum analysis technology;
calculating an envelope surface of joint point coordinates to obtain human motion amplitude based on the human joint motion, and fitting the human motion amplitude by using wavelet transformation to obtain the frequency of joint tremor;
and constructing the post-stroke dyskinesia assessment model based on the human body motion amplitude and the frequency of the joint tremor.
6. A post-stroke cognitive disorder risk assessment model establishing device is characterized by comprising:
the acquisition module is used for acquiring an image data set, a clinical report data set, a voice data set and a video data set of multiple modes of a target population;
the risk analysis module is used for inputting the image data sets of the multiple modes into a pre-constructed stroke segmentation model to obtain the size and position characteristics of a stroke focus of the target population, and analyzing the clinical report data set based on a pre-constructed post-stroke cognitive impairment (PSCI) relevance analysis model to obtain a PSCI risk probability of the target population;
the grading module is used for inputting the voice data set into a pre-constructed post-stroke language dysfunction evaluation model to obtain the language dysfunction grade of the target population, and inputting the video data set into a post-stroke movement dysfunction evaluation model to obtain the movement dysfunction grade of the target population; and
and the generation module is used for training a prediction model formed by a multilayer neural network based on the size and position characteristics of the stroke focus, the PSCI risk probability, the language dysfunction rating and the motor dysfunction rating to obtain a post-stroke cognitive disorder risk assessment model.
7. The apparatus according to claim 6, wherein the risk analysis module, prior to inputting the multi-modal image dataset to a pre-constructed stroke segmentation model, is specifically configured to:
registering the image data sets of the multiple modes by using a medical image registration method to obtain registered image data sets of the multiple modes;
and training a preset convolutional neural network based on the registered multi-modal image data set and the corresponding diagnosis result, and generating the stroke segmentation model.
8. The apparatus of claim 7, wherein prior to analyzing the clinical report dataset based on the post-stroke cognitive impairment (PSCI) relevance analysis model, the risk analysis module is further configured to:
learning, from the clinical report dataset, a demographic characteristic, a correlation of a clinical factor with post-stroke cognitive impairment using a natural language processing algorithm;
and constructing the PSCI relevance analysis model according to the relevance.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the post-stroke cognitive impairment risk assessment model building method of any one of claims 1-5.
10. A computer-readable storage medium having stored thereon a computer program, the program being executable by a processor for implementing the method for post-stroke cognitive impairment risk assessment model building of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210487286.5A CN115089112B (en) | 2022-05-06 | 2022-05-06 | Post-stroke cognitive impairment risk assessment model building method and device and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210487286.5A CN115089112B (en) | 2022-05-06 | 2022-05-06 | Post-stroke cognitive impairment risk assessment model building method and device and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115089112A true CN115089112A (en) | 2022-09-23 |
| CN115089112B CN115089112B (en) | 2024-04-30 |
Family
ID=83287845
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210487286.5A Active CN115089112B (en) | 2022-05-06 | 2022-05-06 | Post-stroke cognitive impairment risk assessment model building method and device and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115089112B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116098587A (en) * | 2023-01-13 | 2023-05-12 | 北京中科睿医信息科技有限公司 | Cognition assessment method, device, equipment and medium based on eye movement |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070299360A1 (en) * | 2006-06-21 | 2007-12-27 | Lexicor Medical Technology, Llc | Systems and Methods for Analyzing and Assessing Dementia and Dementia-Type Disorders |
| CN101299964A (en) * | 2004-04-13 | 2008-11-05 | 剑桥大学技术服务有限公司 | Method for evaluating nervus cognition disorder |
| CN110600053A (en) * | 2019-07-30 | 2019-12-20 | 广东工业大学 | Cerebral stroke dysarthria risk prediction method based on ResNet and LSTM network |
| CN111028863A (en) * | 2019-12-20 | 2020-04-17 | 广州科慧健远医疗科技有限公司 | Method for diagnosing dysarthria tone error after stroke based on neural network and diagnosis device thereof |
| RU2721465C1 (en) * | 2019-07-15 | 2020-05-19 | Федеральное государственное бюджетное военное образовательное учреждение высшего образования "Военно-медицинская академия имени С.М. Кирова" Министерства обороны Российской Федерации (ВМедА) | Method for prediction of development of cognitive disorders in patients in early recovery of ischemic stroke |
| AU2019204376B1 (en) * | 2019-06-21 | 2020-07-02 | Curvebeam Ai Limited | Image Analysis Method and System |
| CN111627567A (en) * | 2020-06-03 | 2020-09-04 | 扬州大学附属医院 | Triangle management theory-based post-stroke cognitive impairment patient remote management system |
| CN112120790A (en) * | 2020-09-23 | 2020-12-25 | 常州市第一人民医院 | Mild ischemic stroke patient prediction model based on index scoring |
| CN212939675U (en) * | 2020-03-16 | 2021-04-13 | 广州市第一人民医院（广州消化疾病中心、广州医科大学附属市一人民医院、华南理工大学附属第二医院） | Post-stroke cognitive disorder evaluation and intervention system |
| CN113331844A (en) * | 2021-06-01 | 2021-09-03 | 郑州大学 | Cerebral stroke conditioning growth system and method based on testing cloud evaluation system |
| CN113598711A (en) * | 2021-08-12 | 2021-11-05 | 四川省医学科学院·四川省人民医院 | System for classifying aphasia after cerebral apoplexy |
| CN113974612A (en) * | 2021-12-02 | 2022-01-28 | 上海大学 | Automatic assessment method and system for upper limb movement function of stroke patient |
| CN114010173A (en) * | 2021-11-01 | 2022-02-08 | 暨南大学附属第一医院（广州华侨医院） | System and method for evaluating non-valvular atrial fibrillation cardiogenic stroke based on neural network |
| CN114224343A (en) * | 2022-01-13 | 2022-03-25 | 平安科技（深圳）有限公司 | Cognitive disorder detection method, device, equipment and storage medium |
-
2022
- 2022-05-06 CN CN202210487286.5A patent/CN115089112B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101299964A (en) * | 2004-04-13 | 2008-11-05 | 剑桥大学技术服务有限公司 | Method for evaluating nervus cognition disorder |
| US20070299360A1 (en) * | 2006-06-21 | 2007-12-27 | Lexicor Medical Technology, Llc | Systems and Methods for Analyzing and Assessing Dementia and Dementia-Type Disorders |
| AU2019204376B1 (en) * | 2019-06-21 | 2020-07-02 | Curvebeam Ai Limited | Image Analysis Method and System |
| RU2721465C1 (en) * | 2019-07-15 | 2020-05-19 | Федеральное государственное бюджетное военное образовательное учреждение высшего образования "Военно-медицинская академия имени С.М. Кирова" Министерства обороны Российской Федерации (ВМедА) | Method for prediction of development of cognitive disorders in patients in early recovery of ischemic stroke |
| CN110600053A (en) * | 2019-07-30 | 2019-12-20 | 广东工业大学 | Cerebral stroke dysarthria risk prediction method based on ResNet and LSTM network |
| CN111028863A (en) * | 2019-12-20 | 2020-04-17 | 广州科慧健远医疗科技有限公司 | Method for diagnosing dysarthria tone error after stroke based on neural network and diagnosis device thereof |
| CN212939675U (en) * | 2020-03-16 | 2021-04-13 | 广州市第一人民医院（广州消化疾病中心、广州医科大学附属市一人民医院、华南理工大学附属第二医院） | Post-stroke cognitive disorder evaluation and intervention system |
| CN111627567A (en) * | 2020-06-03 | 2020-09-04 | 扬州大学附属医院 | Triangle management theory-based post-stroke cognitive impairment patient remote management system |
| CN112120790A (en) * | 2020-09-23 | 2020-12-25 | 常州市第一人民医院 | Mild ischemic stroke patient prediction model based on index scoring |
| CN113331844A (en) * | 2021-06-01 | 2021-09-03 | 郑州大学 | Cerebral stroke conditioning growth system and method based on testing cloud evaluation system |
| CN113598711A (en) * | 2021-08-12 | 2021-11-05 | 四川省医学科学院·四川省人民医院 | System for classifying aphasia after cerebral apoplexy |
| CN114010173A (en) * | 2021-11-01 | 2022-02-08 | 暨南大学附属第一医院（广州华侨医院） | System and method for evaluating non-valvular atrial fibrillation cardiogenic stroke based on neural network |
| CN113974612A (en) * | 2021-12-02 | 2022-01-28 | 上海大学 | Automatic assessment method and system for upper limb movement function of stroke patient |
| CN114224343A (en) * | 2022-01-13 | 2022-03-25 | 平安科技（深圳）有限公司 | Cognitive disorder detection method, device, equipment and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116098587A (en) * | 2023-01-13 | 2023-05-12 | 北京中科睿医信息科技有限公司 | Cognition assessment method, device, equipment and medium based on eye movement |
| CN116098587B (en) * | 2023-01-13 | 2023-10-10 | 北京中科睿医信息科技有限公司 | Cognition assessment method, device, equipment and medium based on eye movement |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115089112B (en) | 2024-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Castellazzi et al. | A machine learning approach for the differential diagnosis of Alzheimer and vascular dementia fed by MRI selected features | |
| KR101818074B1 (en) | Artificial intelligence based medical auto diagnosis auxiliary method and system therefor | |
| US11037070B2 (en) | Diagnostic test planning using machine learning techniques | |
| US10733727B2 (en) | Application of deep learning for medical imaging evaluation | |
| JP2007527743A (en) | System and method for automatic diagnosis and decision support for heart related diseases and conditions | |
| CA2530595A1 (en) | Automated regional myocardial assessment for cardiac imaging | |
| Brugnara et al. | Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis | |
| CN115830017B (en) | Tumor detection system, method, equipment and medium based on image-text multi-mode fusion | |
| US11471096B2 (en) | Automatic computerized joint segmentation and inflammation quantification in MRI | |
| KR102382872B1 (en) | Apparatus and method for medical image reading assistant providing representative image based on medical use artificial neural network | |
| CN112183572B (en) | Method and device for generating prediction model for predicting severity of pneumonia | |
| CN113962930B (en) | Alzheimer disease risk assessment model establishing method and electronic equipment | |
| Xing et al. | Automatic detection of A‐line in lung ultrasound images using deep learning and image processing | |
| CN112561848A (en) | Method, non-transitory computer-readable medium, and apparatus for regional brain age prediction | |
| US9883817B2 (en) | Management, assessment and treatment planning for inflammatory bowel disease | |
| Bhave et al. | Deep learning to detect left ventricular structural abnormalities in chest X-rays | |
| KR102360615B1 (en) | Medical image diagnosis assistance apparatus and method using a plurality of medical image diagnosis algorithm for endoscope images | |
| CN115089112B (en) | Post-stroke cognitive impairment risk assessment model building method and device and electronic equipment | |
| Salih et al. | A review of evaluation approaches for explainable AI with applications in cardiology | |
| CN112200810A (en) | Multi-modal automated ventricular segmentation system and method of use thereof | |
| CN116452592A (en) | Method, device and system for constructing brain vascular disease AI cognitive function evaluation model | |
| WO2020099941A1 (en) | Application of deep learning for medical imaging evaluation | |
| CN112669973B (en) | Disease collaborative progressive prediction method based on big data deep learning and robot | |
| CN114241261A (en) | Image processing-based skin disease identification method, device, equipment and storage medium | |
| CN112992358A (en) | Rehabilitation condition evaluation system and method for traditional Chinese medicine internal medicine patient |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |