Method, device, medium and electronic equipment for predicting risk of depression in post-stroke patients

Abstract

The application relates to a method and device for predicting the risk of depression of a post-stroke patient, a medium and an electronic device. The method comprises: acquiring a brain image of the post-stroke patient and a standard graph of a depression functional network, the standard graph of the depression functional network being used to represent a brain functional network related to post-stroke depression; calculating a network damage score of the post-stroke patient according to the brain image and the standard graph of the depression functional network; acquiring a standard graph of depression structural disconnection, the standard graph of the depression structural disconnection being used to represent a brain disconnection distribution related to post-stroke depression; calculating a structural disconnection score of the post-stroke patient according to the brain image and the standard graph of the depression structural disconnection; inputting the network damage score, the structural disconnection score and clinical information data of the post-stroke patient into a prediction model to determine the risk of depression of the post-stroke patient after several months of onset through the prediction model. The application can predict the risk of depression according to the direct impact of a stroke lesion on a brain network responsible for emotion regulation in the brain.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical treatment, in particular to a method and device for predicting the risk of post-stroke depression of a patient, a medium and an electronic device. BACKGROUND

[0002] Post-stroke depression (PSD) has a high incidence rate, but a low clinical recognition rate, and often cannot be paid attention to by clinicians and patients, which seriously affects the prognosis of patients. Existing prediction methods only focus on the influence of clinical factors and social psychological factors on emotions, and ignore the direct impact of the stroke lesion on the brain network responsible for emotion regulation in the brain at the level of neurobiology.

[0003] Therefore, there is an urgent need for a method for predicting the risk of post-stroke depression of a patient, which can predict the risk of depression to some extent according to the direct impact of the stroke lesion on the brain network responsible for emotion regulation in the brain. SUMMARY

[0004] Embodiments of the present application provide a method, device, computer program product or computer program, computer readable medium and electronic device for predicting the risk of post-stroke depression of a patient, which can predict the risk of depression to some extent according to the direct impact of the stroke lesion on the brain network responsible for emotion regulation in the brain.

[0005] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0006] According to an aspect of an embodiment of the present application, a method for predicting the risk of post-stroke depression of a patient is provided, the method comprising: obtaining a brain image of a post-stroke patient and a depression functional network standard atlas, the depression functional network standard atlas being used to represent a brain functional network related to post-stroke depression; calculating a network damage score of the post-stroke patient according to the brain image and the depression functional network standard atlas; obtaining a depression structural disconnection atlas, the depression structural disconnection atlas being used to represent a brain disconnection distribution related to post-stroke depression; calculating a structural disconnection score of the post-stroke patient according to the brain image and the depression structural disconnection atlas; inputting the network damage score, the structural disconnection score and clinical information data of the post-stroke patient into a prediction model, and determining the risk of depression of the post-stroke patient by the prediction model.

[0007] In some embodiments of the present application, the obtaining of the brain image of the post-stroke patient comprises: obtaining an initial brain image of the post-stroke patient, identifying a stroke lesion area in the initial brain image, converting the initial brain image into a standard brain image format, and generating the brain image of the post-stroke patient.

[0008] In some embodiments of the present application, based on the foregoing scheme, the calculating the network damage score of the post-stroke patient according to the brain image and the standard atlas of functional network of depression comprises: calculating the network damage score of the post-stroke patient according to the overlapping degree of the stroke lesion area of the brain image and the standard atlas of functional network of depression.

[0009] In some embodiments of the present application, based on the foregoing scheme, the calculating the structural disconnection score of the post-stroke patient according to the brain image and the standard atlas of structural disconnection of depression comprises: determining the brain structural disconnection range caused by the lesion according to the stroke lesion area of the brain image; and calculating the structural disconnection score of the post-stroke patient according to the overlapping degree of the structural disconnection range and the standard atlas of structural disconnection of depression.

[0010] In some embodiments of the present application, after inputting the network damage score, the structural disconnection score and the clinical information data of the post-stroke patient into the prediction model, the method further comprises: constructing an initial prediction model; obtaining a model training sample set, the model training sample set comprising the network damage score, the structural disconnection score, the clinical information data and the depression condition after onset of at least one post-stroke patient; and performing model training on the initial prediction model based on a machine learning algorithm based on the model training sample, to obtain a prediction model.

[0011] In some embodiments of the present application, after inputting the network damage score, the structural disconnection score and the clinical information data of the post-stroke patient into the prediction model, the method further comprises: obtaining the age, gender, post-stroke disability degree and cognitive degree of the post-stroke patient as the clinical information data of the post-stroke patient.

[0012] In some embodiments of the present application, the determining the depression risk of the post-stroke patient by the prediction model comprises: determining the post-stroke depression occurrence probability of the post-stroke patient by the prediction model, and determining the depression risk of the post-stroke patient by the post-stroke depression occurrence probability.

[0013] According to an aspect of some embodiments of the present application, there is provided a post-stroke patient depression risk prediction device, the device comprising: a first obtaining unit configured to obtain a brain image of a post-stroke patient and a depression functional network standard atlas, the depression functional network standard atlas being configured to represent a brain functional network related to post-stroke depression; a first calculating unit configured to calculate a network damage score of the post-stroke patient according to the brain image and the depression functional network standard atlas; a second obtaining unit configured to obtain a depression structural disconnection atlas, the depression structural disconnection atlas being configured to represent a brain disconnection distribution related to post-stroke depression; a second calculating unit configured to calculate a structural disconnection score of the post-stroke patient according to the brain image and the depression structural disconnection atlas; and a determining unit configured to input the network damage score, the structural disconnection score, and clinical information data of the post-stroke patient into a prediction model, and determine a depression risk of the post-stroke patient by the prediction model.

[0014] According to an aspect of some embodiments of the present application, there is provided a computer-readable storage medium having at least one program code stored therein, the at least one program code being loaded and executed by a processor to implement the operations performed by the post-stroke patient depression risk prediction method as described.

[0015] According to an aspect of some embodiments of the present application, there is provided an electronic device comprising one or more processors and one or more memories, the one or more memories having at least one program code stored therein, the at least one program code being loaded and executed by the one or more processors to implement the operations performed by the post-stroke patient depression risk prediction method as described.

[0016] Based on the above-mentioned solutions, the present application has at least the following advantages or progresses:

[0017] In the technical solutions provided in some embodiments of the present application, by comparing the brain image of the post-stroke patient, the depression functional network standard atlas, and the depression structural disconnection atlas, the network damage score and the structural disconnection score are calculated respectively, and then the depression risk of the post-stroke patient is predicted by the prediction model. The risk information at the level of neuroanatomy can be integrated with the traditional clinical-demographic factors, so that the performance and generalizability of the prediction model are further improved. Objective biological or imaging markers can also be provided for the diagnosis of mental diseases, which has great clinical practical significance and innovation.

[0018] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0020] It should be apparent that the drawings herein are merely some embodiments of the present application and, for a person of ordinary skill in the art, other drawings can be obtained from these drawings without any creative effort.

[0021] In the drawings:

[0022] Figure 1 A flow chart of a method for predicting depression risk of a post-stroke patient according to an embodiment of the present application is shown;

[0023] Figure 2 A standard graph of a depression functional network according to an embodiment of the present application is shown;

[0024] Figure 3 A flow chart of a method for predicting depression risk of a post-stroke patient according to an embodiment of the present application is shown;

[0025] Figure 4 A schematic diagram of determining a range of structural disconnection of brain caused by a lesion according to an embodiment of the present application is shown;

[0026] Figure 5 A graph of a depression structural disconnection according to an embodiment of the present application is shown;

[0027] Figure 6 A flow chart of a method for predicting depression risk of a post-stroke patient according to an embodiment of the present application is shown;

[0028] Figure 7 A schematic diagram of a software running mechanism according to an embodiment of the present application is shown;

[0029] Figure 8 A device for predicting depression risk of a post-stroke patient according to an embodiment of the present application is shown;

[0030] Figure 9 A structural schematic diagram of a computer system of an electronic device suitable for implementing embodiments of the present application is shown. DETAILED DESCRIPTION

[0031] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art.

[0032] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application can be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, devices, implementations, and operations have not been shown or described in detail to avoid obscuring aspects of the application.

[0033] The block diagrams in the drawings show only the functionality of the features and can not imply that the functions must be implemented in the particular order presented or by the illustrated components. In some embodiments, the functionality of the features can be implemented in hardware, software, or a combination of hardware and software.

[0034] The flow diagrams shown in the various figures, which can also be referred to as flow charts, are provided, generally, as examples only. None of the steps in the drawings are essential or required in all embodiments. Also, the steps can, in some instances, be combined, eliminated or further subdivided. Moreover, the order of the steps can vary, depending on the implementation.

[0035] It should be noted that the term "a plurality of" means two or more. The term "and / or" describes associated objects and means that the associated objects can exist separately, in an "or" relationship or in a combination "and" relationship. The character " / " is generally used to represent "or", unless otherwise noted.

[0036] The implementation details of the technical solutions of the embodiments of the application are described below:

[0037] Please refer to Figure 1 .

[0038] Figure 1 A flow chart of a method for predicting the risk of depression in a post-stroke patient is shown, according to an embodiment of the application, which can include steps S101-S105:

[0039] Step S101, obtaining a brain image of a post-stroke patient and a standard atlas of a depression functional network, the standard atlas of the depression functional network being used to represent a brain functional network related to post-stroke depression.

[0040] Step S102, calculating a network damage score of the post-stroke patient according to the brain image and the standard atlas of the depression functional network.

[0041] Step S103, obtaining a depression structural disconnection map, the depression structural disconnection map being used to represent a brain disconnection distribution related to post-stroke depression.

[0042] Step S104, calculating a structural disconnection score of the post-stroke patient according to the brain image and the depression structural disconnection map.

[0043] Step S105, inputting the network damage score, the structural disconnection score and clinical information data of the post-stroke patient into a prediction model, and determining a depression risk of the post-stroke patient by the prediction model.

[0044] In the present application, by comparing the brain image of the post-stroke patient, the standard functional network map of depression and the depression structural disconnection map, the network damage score and the structural disconnection score are respectively calculated, and then the prediction model is used to predict the depression risk of the post-stroke patient. The risk information at the neuroanatomical level can be integrated with the traditional clinical-demographic factors, so that the performance and generalizability of the prediction model are further improved. Moreover, objective biological or imaging markers can be provided for the diagnosis of mental diseases, which has great clinical practical significance and innovation.

[0045] In the present application, the method for obtaining the brain image of the post-stroke patient comprises: obtaining an initial brain image of the post-stroke patient, identifying a stroke lesion area in the initial brain image, converting the initial brain image into a standard brain image format, and generating the brain image of the post-stroke patient.

[0046] In the present application, the initial brain image of the post-stroke patient can be an MRI or CT image. In actual application, the MRI or CT image of the post-stroke patient can be directly called through the medical software inside the hospital.

[0047] In the present application, the image data management system (PACS system) can be connected through a customized software interface to automatically call and display the brain image data of the post-stroke patient. The doctor can select the required modality. For example, the patient with cerebral infarction induced stroke can use diffusion weighted imaging, the patient with cerebral hemorrhage induced stroke can use CT scan, and the semi-automatic algorithm is used to segment the stroke lesion area. In addition, the doctor can also manually segment the lesion area.

[0048] In the present application, the initial brain image can be converted into a standard brain image format by registering the initial brain image to the internationally unified MNI152 standard space.

[0049] In the present application, the method of calculating the network damage score of the post-stroke patient according to the brain image and the standard atlas of functional network of depression can comprise: calculating the network damage score of the post-stroke patient according to the overlapping degree of the stroke lesion area of the brain image and the standard atlas of functional network of depression.

[0050] In the present application, the standard atlas of functional network of depression can be used to characterize the brain disconnection distribution related to post-stroke depression. For example, reference can be made to Figure 2 , Figure 2 The standard atlas of functional network of depression according to an embodiment of the present application is shown. As shown in Figure 2 The left dorsolateral prefrontal cortex (L-DLPFC) is centered, and the brain regions with positive T values, such as the regions 201, 202, 203, 204, and 205, are positively correlated with the L-DLPFC. The lesions of PSD patients mostly overlap with the regions with positive T values, while the lesions of non-depressed persons generally have less or no overlap with the regions with positive T values.

[0051] In the present application, the method of calculating the network damage score of the post-stroke patient according to the brain image and the standard atlas of functional network of depression can comprise: calculating the network damage score of the post-stroke patient according to the overlapping degree of the stroke lesion area of the brain image and the standard atlas of functional network of depression.

[0052] In the present application, the brain image can be superimposed on the standard atlas of functional network of depression, and then the sum of T statistics of the overlapping part is calculated as the network damage score of the post-stroke patient.

[0053] Reference can be made to Figures 3-5 .

[0054] Figure 3 The flowchart of the method of predicting the depression risk of the post-stroke patient according to an embodiment of the present application is shown. The method of calculating the structural disconnection score of the post-stroke patient according to the brain image and the standard atlas of structural disconnection of depression can comprise steps S301-S302:

[0055] Step S301, determining the brain structural disconnection range caused by the lesion according to the stroke lesion area of the brain image.

[0056] Step S302, calculating the structural disconnection score of the post-stroke patient according to the structural disconnection range and the overlapping degree of the standard atlas of structural disconnection of depression.

[0057] Figure 4 The schematic diagram of determining the brain structural disconnection range caused by the lesion according to an embodiment of the present application is shown. As shown in Figure 4As shown in FIG. A, the spatially standardized lesion can be superimposed on the open-source structural connectivity atlas published by the Human Connectome Project to obtain the structural disconnection range caused by the lesion. As shown in FIG. B, the structural disconnection severity map is shown, and the value of each voxel represents the percentage of disconnected fibers in the voxel relative to the total number of fibers in the voxel.

[0058] Figure 5 As shown in FIG. 3, the structural disconnection map of depression according to an embodiment of the present application is shown. Figure 5 As shown, the more white matter fibers in the 501-509 regions are disconnected, the higher the risk and severity of depression after stroke.

[0059] In the present application, the structural disconnection range caused by the lesion can be superimposed on the structural disconnection map of depression to determine the high-risk area of the structural disconnection range and the structural disconnection map of depression, i.e. Figure 5 the superimposition degree of the 501-509 regions in FIG. 1, to calculate the structural disconnection score of the patient after stroke.

[0060] Please refer to Figure 6 .

[0061] Figure 6 As shown in FIG. 4, a flowchart of a method for predicting the risk of depression in a patient after stroke according to an embodiment of the present application is shown. The method can further include steps S601-S603 after the network damage score, the structural disconnection score, and the clinical information data of the patient after stroke are input into the prediction model.

[0062] Step S601, constructing an initial prediction model.

[0063] Step S602, obtaining a model training sample set, the model training sample set including the network damage score, the structural disconnection score, the clinical information data, and the depression condition after onset of at least one patient after stroke.

[0064] Step S603, based on the model training sample, performing model training on the initial prediction model based on a machine learning algorithm to obtain a prediction model.

[0065] In the present application, since the foregoing scheme can predict the risk of depression in a patient after stroke to a certain extent, the condition of the patient is very diverse, and the model needs to be trained according to big data to improve the accuracy, sensitivity, and specificity of the prediction model.

[0066] In the present application, since the time or degree of depression of the post-stroke patients is different, different initial model parameters can be used for different training samples, and the initial model modified by the parameters can be closer to the actual situation of each training sample, and also can make each training sample normalized based on the machine learning algorithm.

[0067] In the present application, the network damage score, the structure disconnection score, the clinical information data, and the post-stroke depression can be used as the predictor to train the initial prediction model of post-stroke, and the training process can use various machine learning algorithms such as support vector machine, decision tree, artificial neural network, and ensemble learning, and select the optimal model as the final model.

[0068] In the present application, the network damage score, the structure disconnection score, and the clinical information data of the post-stroke patient are input into the prediction model, and the method can further include: obtaining the age, gender, post-stroke disability degree, and cognitive degree of the post-stroke patient as the clinical information data of the post-stroke patient.

[0069] In the present application, the method for determining the depression risk of the post-stroke patient by the prediction model includes: determining the post-stroke depression occurrence probability of the post-stroke patient by the prediction model, and determining the depression risk of the post-stroke patient by the post-stroke depression occurrence probability.

[0070] In the present application, the depression risk can be divided into three risk levels of low, medium, and high.

[0071] Please refer to Figure 7 , Figure 7 The software running mechanism diagram according to one embodiment of the present application is shown. As Figure 7 shown, the technical method of the present application can be integrated into a one-stop software, and the running mechanism of the software is as follows:

[0072] (1) The clinician inputs the unique identification number (such as the hospitalization number) of a specific patient in the software interface;

[0073] (2) Connect the medical record system (HIS system) through the customized software interface, and use the natural language processing algorithm of artificial intelligence to quickly extract the basic clinical information data of the patient, such as age, gender, scale (post-stroke disability degree and cognitive function, etc.), which is automatically implemented without the need for the physician to operate;

[0074] (3) Through the customized software interface connection image data management system (PACS system), automatically call and display the brain image data of the patient, the doctor selects the required mode (diffusion weighted imaging for cerebral infarction, CT scan for cerebral hemorrhage), the software uses a semi-automatic algorithm to segment the lesion area, and the software also has a manual segmentation function (manual segmentation is the gold standard for lesion segmentation);

[0075] (4) Spatial standardization of the lesion: the software automatically registers the initial brain image and the segmented lesion mask to the internationally unified MNI152 standard space;

[0076] (5) Calculate the network damage score: the software superimposes the registered lesion on the standard functional network atlas of depression, and calculates the sum of the T statistics of the overlapping part as the network damage score.

[0077] (6) Calculate the structural disconnection score: the software superimposes the registered brain image on the "Human Connectome Project-842" fiber connection atlas, and clearly shows the fiber interrupted by the lesion, which is the structural disconnection range caused by the lesion; superimpose the range on the structural disconnection atlas of depression, and the sum of the Z statistics of the overlapping part is the structural disconnection score.

[0078] (7) Risk prediction: the software integrates the functional network damage score, the structural disconnection score and the extracted clinical demographic information, inputs into the machine learning model to calculate the risk of post-stroke depression, and the software interface displays the probability and risk level of the patient's future occurrence of post-stroke depression.

[0079] (8) The doctor makes clinical decisions according to the prediction results, and gives biological-psychological-social multi-level PSD prevention and treatment to high-risk groups, including psychological treatment, social support, antidepressants, neural regulation, etc.

[0080] Next, an embodiment of an apparatus of the present application will be described in conjunction with the accompanying drawings.

[0081] Please refer to Figure 8 .

[0082] Figure 8 A post-stroke patient depression risk prediction apparatus according to an embodiment of the present application is shown, and the apparatus 800 can include a first acquisition unit 801, a first calculation unit 802, a second acquisition unit 803, a second calculation unit 804, and a determination unit 805.

[0083] The apparatus 800 can be specifically configured as: a first obtaining unit 801 configured to obtain a brain image of a post-stroke patient and a depression functional network standard graph, the depression functional network standard graph being used to represent a brain functional network related to post-stroke depression; a first calculating unit 802 configured to calculate a network damage score of the post-stroke patient according to the brain image and the depression functional network standard graph; a second obtaining unit 803 configured to obtain a depression structural disconnection graph, the depression structural disconnection graph being used to represent a brain disconnection distribution related to post-stroke depression; a second calculating unit 804 configured to calculate a structural disconnection score of the post-stroke patient according to the brain image and the depression structural disconnection graph; and a determining unit 805 configured to input the network damage score, the structural disconnection score, and clinical information data of the post-stroke patient into a prediction model, and determine a depression risk of the post-stroke patient by using the prediction model.

[0084] Next, refer to Figure 9 .

[0085] Figure 9 A structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application is shown.

[0086] It should be noted that, Figure 9 The computer system 900 of the electronic device shown is only an example, and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0087] As Figure 9 shown, the computer system 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 902 or programs loaded from a storage portion 908 to a random access memory (RAM) 903, such as performing the methods described in the above embodiments. In the RAM 903, various programs and data required for system operation are also stored. The CPU 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0088] The following components are connected to the I / O interface 905: an input part 906 including a keyboard, a mouse, etc.; an output part 907 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage part 908 including a hard disk, etc.; and a communication part 909 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication part 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 910 as necessary, so that a computer program read out therefrom is installed in the storage part 908 as necessary.

[0089] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for executing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the central processing unit (CPU) 901, various functions defined in the system of the present application are executed.

[0090] It should be noted that the computer-readable medium in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples of the computer-readable storage medium can 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), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present application, the computer-readable signal medium can include a data signal carrying computer-readable program code in a baseband or as a part of a carrier wave. Such a propagated data signal can take on various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium that can send, propagate or transmit a program for use by or in connection with an instruction execution system, device or apparatus. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, or the like, or any suitable combination thereof.

[0091] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In the flowcharts or block diagrams, each block can represent a module, a program segment or a part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than that shown in the drawings. For example, two blocks that are shown in succession can actually be executed substantially in parallel, and they can also be executed in reverse order, depending on the involved functions. It should also be noted that each block in the block diagrams or flowcharts, and the combination of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0092] The units described in the embodiments of the present application can be implemented in the form of software, or can be implemented in the form of hardware, and the units described can also be arranged in a processor. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0093] As another aspect, the present application also provides a computer program product or computer program, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method for predicting the risk of depression of a post-stroke patient described in the above embodiments.

[0094] As another aspect, the present application also provides a computer readable medium, which can be included in the electronic device described in the above embodiments, or can exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the method for predicting the risk of depression of a post-stroke patient described in the above embodiments.

[0095] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into several modules or units.

[0096] From the above description of the embodiments, those skilled in the art will readily appreciate that the example embodiments described herein can be implemented by software and / or by hardware coupled with software. Accordingly, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, U disk, mobile hard disk, etc.) or network, and includes several instructions to make a computing device (which can be a personal computer, server, touch terminal, or network device, etc.) execute the methods according to the embodiments of the present application.

[0097] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the application following the general principles thereof and including such departures from the present disclosure as come within known use or custom in the art.

[0098] It is to be understood that the application is not limited to the precise construction already described above and shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application should only be limited by the claims appended hereto.

Claims

1. A method of predicting the risk of depression in a post-stroke patient, characterized in that, The method comprises: obtaining a brain image of a post-stroke patient and a standard atlas of a depressive functional network, the standard atlas of the depressive functional network being used to represent a brain functional network related to post-stroke depression; calculating a network damage score of the post-stroke patient according to the brain image and the standard atlas of the depressive functional network, comprising: calculating the network damage score of the post-stroke patient according to an overlapping degree of a stroke lesion area of the brain image and the standard atlas of the depressive functional network; obtaining a standard atlas of depressive structural disconnection, the standard atlas of depressive structural disconnection being used to represent a brain disconnection distribution related to post-stroke depression; calculating a structural disconnection score of the post-stroke patient according to the brain image and the standard atlas of depressive structural disconnection, comprising: determining a brain structural disconnection range caused by a lesion according to a stroke lesion area of the brain image; and calculating the structural disconnection score of the post-stroke patient according to an overlapping degree of the brain structural disconnection range and the standard atlas of depressive structural disconnection; inputting the network damage score, the structural disconnection score, and clinical information data of the post-stroke patient into a prediction model to determine a depression risk of the post-stroke patient by the prediction model; The method further comprises: obtaining an initial brain image of a post-stroke patient, identifying a stroke lesion area in the initial brain image, converting the initial brain image into a standard brain image format, and generating a brain image of the post-stroke patient.

2. The method of claim 1, wherein, The method further comprises: constructing an initial prediction model; obtaining a model training sample set, the model training sample set comprising a network damage score, a structural disconnection score, clinical information data, and a post-onset depression condition of at least one post-stroke patient; performing model training on the initial prediction model based on a machine learning algorithm based on the model training sample to obtain a prediction model.

3. The method of claim 1, wherein, The method further comprises: obtaining age, gender, post-stroke disability degree, and cognitive degree of the post-stroke patient as the clinical information data of the post-stroke patient.

4. The method of claim 1, wherein, The method further comprises: determining a post-stroke depression occurrence probability of the post-stroke patient by the prediction model, and determining the depression risk of the post-stroke patient by the post-stroke depression occurrence probability.

5. A post-stroke patient depression risk prediction apparatus, characterized by, The device comprises: a first obtaining unit configured to obtain a brain image of a post-stroke patient and a standard atlas of a depressive functional network, the standard atlas of the depressive functional network being used to represent a brain functional network related to post-stroke depression; The first calculation unit is configured to calculate a network damage score of the post-stroke patient according to the brain image and the standard functional network map of depression, including: calculating the network damage score of the post-stroke patient according to the overlapping degree of the stroke lesion area of the brain image and the standard functional network map of depression. The second acquisition unit is configured to acquire a structural disconnection map of depression, the structural disconnection map of depression being used to represent the brain disconnection distribution related to post-stroke depression, including: determining a brain structure disconnection range caused by a lesion according to the stroke lesion area of the brain image; and calculating a structural disconnection score of the post-stroke patient according to the overlapping degree of the brain structure disconnection range and the structural disconnection map of depression. The second calculation unit is configured to calculate a structural disconnection score of the post-stroke patient according to the brain image and the structural disconnection map of depression. The determination unit is configured to input the network damage score, the structural disconnection score and clinical information data of the post-stroke patient into a prediction model, and determine the depression risk of the post-stroke patient by the prediction model. The brain image of the post-stroke patient is acquired, including: An initial brain image of the post-stroke patient is acquired, a stroke lesion area in the initial brain image is identified, the initial brain image is converted into a standard brain image format, and the brain image of the post-stroke patient is generated.

6. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the operations performed by the prediction method of the depression risk of the post-stroke patient according to any one of claims 1 to 4.

7. An electronic device, comprising: The electronic device includes one or more processors and one or more memories, and the one or more memories store at least one program code, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the prediction method of the depression risk of the post-stroke patient according to any one of claims 1 to 4.

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