CN115910379B - Method, system, equipment and storage medium for evaluating curative effect after kidney stone operation - Google Patents
Method, system, equipment and storage medium for evaluating curative effect after kidney stone operation Download PDFInfo
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Abstract
The invention relates to a method, a system, equipment and a storage medium for evaluating the curative effect after kidney stone operation, wherein the method comprises the steps of acquiring patient data and a first characteristic; determining patient target data according to a preset data normalization rule and patient data; determining a second characteristic according to a preset characteristic determining rule and patient target data; determining a target prediction model according to a preset training model, a first characteristic, a second characteristic and patient target data; and obtaining patient data to be predicted, and determining the postoperative efficacy of the patient according to the patient data to be predicted and the target prediction model. The method has the effect of improving the accuracy of in-vitro shock wave lithotripsy curative effect prediction.
Description
Technical Field
The application relates to the technical field of efficacy evaluation, in particular to a method, a system, equipment and a storage medium for evaluating the efficacy after kidney stone operation.
Background
In vitro shock wave lithotripsy (ESWL) therapy is one of the common therapeutic methods for kidney stones, and for patients with kidney stones, if the stone volume is not very large, no serious obstruction is formed, it is possible to treat the patient by extracorporeal shock wave lithotripsy. Most patients may break up stones into smaller particles which are then discharged themselves. However, clinical practice has shown that not all stones are suitable for Extracorporeal Shock Wave Lithotripsy (ESWL), some stones cannot be broken, and some stones cannot be discharged by the patient after being broken.
For the patient planning to implement the extracorporeal shock wave lithotripsy, the existing curative effect prediction method mainly measures parameters such as distance between the lithotripsy and the body surface, CT value and the like from CT image data, so as to predict success rate of Extracorporeal Shock Wave Lithotripsy (ESWL), and related researches are also carried out to predict curative effect by establishing an artificial intelligent model. On one hand, the CT value and other related data of a patient need to be measured manually, so that the prediction result of the curative effect is inaccurate to a certain extent, on the other hand, the current factors of the curative effect prediction reference are too few, and the factors considered in the process of the curative effect prediction are not comprehensive, so that the curative effect prediction is inaccurate.
The prior art solutions described above have the following drawbacks: there is a problem in that the prediction of the efficacy of Extracorporeal Shock Wave Lithotripsy (ESWL) is inaccurate.
Disclosure of Invention
In order to solve the problem of inaccurate in-vitro shock wave lithotripsy curative effect prediction, the application provides a method, a system, equipment and a storage medium for evaluating the curative effect after kidney stone operation.
In a first aspect of the present application, a method of evaluating the efficacy of a kidney stone postoperative treatment is provided. The method comprises the following steps:
acquiring patient data and a first feature;
determining patient target data according to a preset data normalization rule and the patient data;
determining a second characteristic according to a preset characteristic determining rule and the patient target data;
determining a target prediction model according to a preset training model, the first characteristic, the second characteristic and the patient target data;
and obtaining patient data to be predicted, and determining the postoperative efficacy of the patient according to the patient data to be predicted and the target prediction model.
According to the technical scheme, the patient data and the first characteristics are obtained, the patient data are processed according to the data normalization rule, the patient target data are determined, the patient target data are screened according to the characteristic determination rule, the second characteristics are determined, then the target prediction model is determined according to the preset training model, the first characteristics, the second characteristics and the patient target data, the patient data of the patient to be predicted are obtained, the patient data of the patient to be predicted are input into the target prediction model, the postoperative curative effect of the patient can be obtained, the problem that the external shock wave lithotripsy curative effect prediction is inaccurate can be solved through training the target prediction model, and the accuracy of the external shock wave lithotripsy curative effect prediction can be improved to a certain extent.
In one possible implementation manner, the acquiring the first feature includes:
obtaining a kidney segmentation model;
and determining a first characteristic according to a preset training rule, the patient data and the kidney segmentation model.
In one possible implementation, the acquiring patient data includes: the patient data includes a stone occurrence image;
acquiring image data and a labeling instruction, wherein the image data comprises a plain CT image and an enhanced CT image of a patient;
and labeling the image data according to the labeling instruction and the image data, and determining a calculus occurrence image.
In one possible implementation, determining patient target data according to a preset data normalization rule and the patient data;
the patient data further includes numerical data;
the patient target data comprises target numerical value data and target image data;
determining target numerical data according to a standard score rule and the numerical data;
and determining target image data according to the resampling rule and the calculus occurrence image.
In one possible implementation manner, the determining the second feature according to the preset feature determining rule and the patient target data includes:
determining target characteristics according to the characteristic extraction rules and the patient target data;
and determining a second feature according to the feature screening rule and the target feature.
In one possible implementation, the method further includes:
acquiring a test data set;
determining a result data set according to the test data set and the target prediction model;
and determining the prediction accuracy according to accuracy calculation rules, the test data set and the result data set.
In one possible implementation, the training rule is a support vector machine.
In a second aspect of the present application, a system for evaluating the efficacy of a kidney stone postoperative treatment is provided. The system comprises:
the data acquisition module is used for acquiring patient data, the first characteristics and the patient data to be predicted;
the data processing module is used for determining patient target data according to a preset data normalization rule and the patient data;
the feature extraction module is used for determining a second feature according to a preset feature determination rule and the patient target data;
the model training module is used for determining a target prediction model according to a preset training model, the first characteristic, the second characteristic and the patient target data;
and the curative effect prediction module is used for determining the postoperative curative effect of the patient according to the correct rate calculation rule, the patient data to be predicted and the target prediction model.
In a third aspect of the present application, an electronic device is provided. The electronic device includes: the device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the method for evaluating the curative effect after the kidney stone operation when executing the program.
In a fourth aspect of the present application, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as according to the first aspect of the present application.
In summary, the present application includes at least one of the following beneficial technical effects:
1. acquiring patient data and first characteristics, processing the patient data according to a data normalization rule, determining patient target data, screening the patient target data according to a characteristic determination rule, determining second characteristics, determining a target prediction model according to a preset training model, the first characteristics, the second characteristics and the patient target data, acquiring patient data of a patient to be predicted, inputting the patient data of the patient to be predicted into the target prediction model, obtaining postoperative efficacy of the patient, improving the problem of inaccurate in-vitro shock wave lithotripsy efficacy prediction by training the target prediction model, and improving the accuracy of in-vitro shock wave lithotripsy efficacy prediction to a certain extent;
2. and testing the target prediction model by acquiring a test data set to obtain a result data set, and calculating the prediction accuracy corresponding to the target prediction model according to the accuracy calculation rule and the result data set. And testing the target prediction model to further verify the reliability of the model.
Drawings
Fig. 1 is a flow chart of a method for evaluating the efficacy of a kidney stone operation provided in the present application.
Fig. 2 is a schematic structural diagram of the system for evaluating the postoperative efficacy of kidney stones provided in the present application.
Fig. 3 is a schematic structural diagram of an electronic device provided in the present application.
In the figure, 200, a kidney stone postoperative efficacy evaluation system; 201. a data acquisition module; 202. a data processing module; 203. a feature extraction module; 204. a model training module; 205. a efficacy prediction module; 301. a CPU; 302. a ROM; 303. a RAM; 304. an I/O interface; 305. an input section; 306. an output section; 307. a storage section; 308. a communication section; 309. a driver; 310. removable media.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
The embodiment of the application provides a method for evaluating the curative effect after kidney stone operation, and the main flow of the method is described as follows.
As shown in fig. 1:
step S101: patient data and a first characteristic are acquired.
Specifically, firstly, plain scan CT image data, enhanced CT image data, clinical statistics, CT manual measurement indexes of a patient and postoperative efficacy conditions of the corresponding patient are acquired. The clinician marks the flat scanning CT image data and the enhanced CT image data according to the flat scanning CT image data and the enhanced CT image data of the patient, marks the positions where kidney stones are generated on the flat scanning CT image data and the enhanced CT image data, and marks the positions as stone occurrence images. The clinical statistics include, but are not limited to, patient height (cm), weight (kg), pulse (times/minutes), blood pressure (KPa), and the like. The CT manual measurement indexes comprise the position, the shape and the size of the calculus. The postoperative efficacy conditions of the corresponding patients indicate the sizes of stones in the body after the patients finish extracorporeal shock wave lithotripsy treatment, doctors can make efficacy marks on the data of the patients according to the sizes of the stones, and the efficacy marks comprise remarkable efficacy and no efficacy. Patient data includes stone occurrence images, clinical statistics, CT manual measurement index, and efficacy identification. According to the above description, it can be known that the clinical statistics and the CT manual measurement index are numerical data, so that the clinical statistics and the CT manual measurement index form numerical data.
The obtaining of the first feature includes: first, an nn-Unet kidney segmentation model based on KiTS19 dataset. The nn-Unet kidney segmentation model based on the KiTS19 data set is an open source, the calculus occurrence image in the patient data is input into the nn-Unet kidney segmentation model, breakpoint training of preset value times is carried out, and the target kidney segmentation model is determined. When the efficacy of a patient is to be predicted, a stone occurrence image of the patient to be predicted is input to the target kidney segmentation model, and the target kidney segmentation model outputs a set of matrix data corresponding to the patient. The matrix data is a first feature. In this embodiment, the preset value is 50, that is, 50 times of breakpoint training is performed on the nn-Unet kidney segmentation model.
Step S102: and determining patient target data according to a preset data normalization rule and patient data.
Specifically, the patient target data includes target numerical data and target image data. The patient data includes stone occurrence images and numerical data. And resampling the calculus occurrence image to obtain target image data, and determining target numerical data according to a standard score rule and the numerical data. The mean and standard deviation of the above numerical data are obtained, and target numerical data= (numerical data-mean)/standard deviation. For example, if the numerical data is the height of a patient, the average and standard deviation of the heights of all patients are calculated, and the target numerical data corresponding to the height= (height-average)/standard deviation.
Step S103: and determining a second characteristic according to a preset characteristic determination rule and patient target data.
Specifically, the second feature includes an invalid target feature and a valid target feature. Classifying all patient target data according to the curative effect identifiers, wherein the patient target data with obvious curative effect is identified as curative effect target data to form a curative effect obvious data set, and the patient target data with curative effect is identified as non-curative effect to form a non-curative effect data set. And respectively extracting image histology characteristics of the target image data in the curative effect significant data set and the curative effect non-data set, extracting 1688 image histology characteristics, and classifying the image histology characteristics into four categories of first-order statistics, shape, texture and filtering. The four classified image histology features include 1688 features, which are well known to those skilled in the art and will not be described in detail herein. The image histology characteristics obtained by the data set with obvious curative effect form effective characteristics, and the image histology characteristics obtained by the data set without curative effect form ineffective characteristics. The target features include the active features and the inactive features. And determining invalid target features and valid target features according to the feature screening rules, the valid features and the invalid features. The feature screening rules include variance thresholding, spline correlation analysis, and LASSO regression models. In the embodiment, feature selection is carried out on the effective features sequentially through a variance threshold method, a spin correlation analysis method and a LASSO regression model, and effective target features are determined; and carrying out feature selection on the invalid features sequentially through a variance threshold method, a spin correlation analysis method and a LASSO regression model to determine the invalid target features. And the selected invalid target feature and the effective target feature are the same features, and the invalid target feature or the effective target feature is used as an optimal feature, namely a second feature. The image histology feature extraction, variance thresholding, spline correlation analysis and LASSO regression model are all techniques known to those skilled in the art, and are not described in detail herein. The optimal features refer to features which have a larger influence on the predicted curative effect in all the image histology features.
Step S104: and determining a target prediction model according to the preset training model, the first characteristic, the second characteristic and the target data of the patient.
Specifically, the first feature, the second feature and the patient target data are input into a preset training model, the target prediction model is obtained through training, and the training model is a support vector machine.
Step S105: and acquiring a test data set, and determining the prediction accuracy according to the accuracy calculation rule and the test data set.
Specifically, a test data set is obtained, the data type of the test data set is the same as that of patient data, the difference between the test data set and the patient data is that the patient has different disease time, a certain time node is taken as a demarcation line, the disease data of the patient before the time node is the patient data used by the training target prediction model, and the disease data of the patient after the time node is the test data set used by the testing target prediction model. The time node is set manually. And determining a result data set according to the test data set and the target prediction model. And inputting the test data set into the target prediction model to obtain the treatment effect of each patient, wherein the treatment effect forms a result data set. And determining the prediction accuracy according to the accuracy calculation rule, the test data set and the result data set. The result dataset is compared with the test dataset to obtain the predicted correct number of patients, the predicted correct rate = predicted correct number of patients/number of patients of the test dataset. For example, the test dataset has 10 patient data, wherein 5 patients are inactive treatments, 5 patients are active treatments, the test dataset is input to the target prediction model to obtain a result dataset, 8 patients in the result dataset are active treatments, 2 patients are inactive treatments, wherein 5 of the 8 patients are active treatments, i.e. 5 patients are correctly predicted, 2 patients are correctly predicted for inactive treatments, the number of correctly predicted patients is 7, and the prediction accuracy is 7/10=0.7.
When a target prediction model is used for predicting the postoperative efficacy of a patient, patient data to be predicted is obtained, the patient data to be predicted is the same as the patient data in type, a calculus occurrence image in the patient data to be predicted is input into the target kidney segmentation model to obtain a first characteristic corresponding to the patient, and then a second characteristic corresponding to the patient is determined according to a characteristic determination rule and the calculus occurrence image. And then when the patient data to be predicted, the corresponding first characteristic and the second characteristic are input into a target prediction model, the target prediction model outputs predicted curative effects, wherein the predicted curative effects comprise two prediction results of obvious curative effects and no curative effects.
An embodiment of the present application provides a system 200 for evaluating a post-operative treatment effect of a kidney stone, referring to fig. 2, the system 200 for evaluating a post-operative treatment effect of a kidney stone includes:
a data acquisition module 201 for acquiring patient data, a first feature and patient data to be predicted;
a data processing module 202, configured to determine patient target data according to a preset data normalization rule and the patient data;
the feature extraction module 203 is configured to determine a second feature according to a preset feature determination rule and the patient target data;
the model training module 204 is configured to determine a target prediction model according to a preset training model, the first feature, the second feature, and the patient target data;
the efficacy prediction module 205 is configured to determine a post-operative efficacy of the patient according to a correct rate calculation rule, the patient data to be predicted, and the target prediction model.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the described module, which is not described herein again.
The embodiment of the application discloses electronic equipment. Referring to fig. 3, the electronic device includes a Central Processing Unit (CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage portion 307 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other by a bus. An input/output (I/O) interface 304 is also connected to the bus.
The following components are connected to the I/O interface 304: an input section 305 including a keyboard, a mouse, and the like; an output portion 306 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 307 including a hard disk and the like; and a communication section 308 including a network interface card such as a LAN card, a modem, or the like. The communication section 308 performs communication processing via a network such as the internet. A driver 309 is also connected to the I/O interface 304 as needed. A removable medium 310 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 309 as needed, so that a computer program read out therefrom is installed into the storage section 307 as needed.
In particular, according to embodiments of the present application, the process described above with reference to flowchart fig. 1 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such embodiments, the computer program may be downloaded and installed from a network via the communication portion 308, and/or installed from the removable media 310. The above-described functions defined in the apparatus of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 301.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the application referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or their equivalents is possible without departing from the spirit of the application. Such as the above-mentioned features and the technical features having similar functions (but not limited to) applied for in this application are replaced with each other.
Claims (6)
1. A method for evaluating the efficacy of a kidney stone operation, comprising:
acquiring patient data and a first feature, wherein the patient data comprises a calculus occurrence image, numerical data and a curative effect identifier, the numerical data comprises clinical statistics data and CT manual measurement indexes, and the first feature is matrix data;
the acquiring the first feature includes: obtaining a kidney segmentation model; determining a first feature according to a preset training rule, the patient data and the kidney segmentation model;
the acquiring patient data includes: acquiring image data and a labeling instruction, wherein the image data comprises a plain CT image and an enhanced CT image of a patient; labeling the image data according to the labeling instruction and the image data, and determining a calculus occurrence image;
determining patient target data according to a preset data normalization rule and the patient data, wherein the method comprises the following steps:
the patient target data comprises target numerical value data and target image data;
determining target numerical data according to a standard score rule and the numerical data;
resampling the calculus occurrence image to obtain target image data;
determining a second feature according to a preset feature determination rule and the patient target data, wherein the second feature comprises an invalid target feature and a valid target feature, and the method comprises the following steps:
classifying the patient target data according to the curative effect identification, and determining a curative effect significant data set and a non-curative effect data set;
respectively extracting image group science characteristics of the target image data in the curative effect significant data set and the curative effect non-data set, and determining effective characteristics and ineffective characteristics;
determining invalid target features and valid target features according to feature screening rules, the valid features and the invalid features;
determining a target prediction model according to a preset training model, the first characteristic, the second characteristic and the patient target data;
and obtaining patient data to be predicted, and determining the postoperative efficacy of the patient according to the patient data to be predicted and the target prediction model.
2. The method for evaluating the efficacy of a kidney stone surgery according to claim 1, further comprising:
acquiring a test data set;
determining a result data set according to the test data set and the target prediction model;
and determining the prediction accuracy according to accuracy calculation rules, the test data set and the result data set.
3. The method for evaluating the efficacy of a kidney stone surgery according to claim 1, wherein the training rule is a support vector machine.
4. A system for evaluating the efficacy of a kidney stone postoperative treatment, comprising:
the data acquisition module is used for acquiring patient data, first characteristics and patient data to be predicted, wherein the patient data comprises a calculus occurrence image, numerical data and a curative effect identifier, the numerical data comprises clinical statistics data and CT manual measurement indexes, and the first characteristics are matrix data; the acquiring the first feature includes: obtaining a kidney segmentation model; determining a first feature according to a preset training rule, the patient data and the kidney segmentation model; the acquiring patient data includes: acquiring image data and a labeling instruction, wherein the image data comprises a plain CT image and an enhanced CT image of a patient; labeling the image data according to the labeling instruction and the image data, and determining a calculus occurrence image;
the data processing module is used for determining patient target data according to a preset data normalization rule and the patient data, and comprises the following steps: the patient target data comprises target numerical value data and target image data; determining target numerical data according to a standard score rule and the numerical data; resampling the calculus occurrence image to obtain target image data;
the feature extraction module is configured to determine a second feature according to a preset feature determination rule and the patient target data, where the second feature includes an invalid target feature and a valid target feature, and includes: classifying the patient target data according to the curative effect identification, and determining a curative effect significant data set and a non-curative effect data set; respectively extracting image group science characteristics of the target image data in the curative effect significant data set and the curative effect non-data set, and determining effective characteristics and ineffective characteristics; determining invalid target features and valid target features according to feature screening rules, the valid features and the invalid features;
the model training module is used for determining a target prediction model according to a preset training model, the first characteristic, the second characteristic and the patient target data;
and the curative effect prediction module is used for determining the postoperative curative effect of the patient according to the correct rate calculation rule, the patient data to be predicted and the target prediction model.
5. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program capable of being loaded by the processor and performing the method according to any of claims 1 to 3.
6. A computer readable storage medium, characterized in that a computer program is stored which can be loaded by a processor and which performs the method according to any of claims 1 to 3.
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| CN112951406A (en) * | 2021-01-27 | 2021-06-11 | 安徽理工大学 | Lung cancer prognosis auxiliary evaluation method and system based on CT (computed tomography) image omics |
| CN115526824A (en) * | 2022-01-27 | 2022-12-27 | 深圳惟德精准医疗科技有限公司 | Image processing method and related device |
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Denomination of invention: A method, system, equipment, and storage medium for evaluating the postoperative efficacy of kidney stones Granted publication date: 20230602 Pledgee: Haidian Beijing science and technology enterprise financing Company limited by guarantee Pledgor: Huiying medical technology (Beijing) Co.,Ltd. Registration number: Y2024110000065 |