Prosopalgia onset risk prediction method and system

Abstract

The invention relates to the technical field of disease diagnosis of the pain department, in particular to a prosopalgia onset risk prediction method and system. The trigeminal neuralgia can be effectively evaluated by combining the multiple groups of indexes and the image group feature data, the score data reflecting the trigeminal neuralgia can be obtained, and the probability and the attack degree of the trigeminal neuralgia can be more intuitively determined through the score data, so that the overall automatic prediction of the trigeminal neuralgia is realized.

Description

Technical Field

[0001] The present application relates to the technical field of pain disease diagnosis, and specifically to a method and system for predicting the risk of trigeminal neuralgia. Background Art

[0002] Trigeminal neuralgia (TN) is characterized by unilateral, short-lived, shock-like paroxysmal pain in the distribution of one or more branches of the trigeminal nerve. The diagnosis of TN is based on three main clinical symptoms: pain localized to one or more areas of the trigeminal nerve distribution; sudden and severe pain of very short duration, described as "shock" or "electrical"; and pain that can be elicited by innocuous stimulation of the trigeminal nerve distribution in the face or mouth.

[0003] Although the cause of TN is known and the clinical symptoms are clear, TN is sometimes misdiagnosed in the prior art due to its low incidence. Summary of the Invention

[0004] In order to address the technical gaps in the existing technology, the embodiments of the present application provide an auxiliary diagnosis method and system for the diagnosis of trigeminal neuralgia by combining computer medical images and multiple clinical indicators. By extracting imaging group features from images, the extracted features and clinical data are combined to realize automated risk assessment based on a predictive model.

[0005] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0006] In a first aspect, a method for predicting the risk of trigeminal neuralgia is provided, the method comprising: obtaining clinical data of a patient to be diagnosed and imaging group characteristic data of a target area of ​​the patient to be diagnosed; the imaging group characteristic data is MRI image characteristic data, and the clinical data is a neurovascular compression degree value, a neurovascular compression site weight value, a vertical height value of the trigeminal nerve cistern segment, an enlarged cross-sectional area value of the trigeminal nerve cistern segment, and a nerve-pons angle value; the clinical data and the imaging group characteristic data are input into a risk prediction model to calculate a risk value; the risk prediction model is a nomogram model.

[0007] Furthermore, the image group feature data includes multiple first-order features, neighborhood grayscale difference matrix busyness, multiple grayscale co-occurrence matrix features, multiple grayscale region matrix features, and grayscale correlation matrix dependent variance.

[0008] Furthermore, the multiple first-order features include a first-order minimum value, a first-order tenth percentile, a first-order energy value, a first-order ninetieth percentile, a first-order total energy value, a first-order interquartile range, a first-order skewness value, a first-order range, and a first-order mean absolute deviation.

[0009] Furthermore, the plurality of gray-level co-occurrence matrix features include differential entropy, inverse variance, correlation, cluster prominence and autocorrelation.

[0010] Furthermore, the plurality of grayscale size region matrix features include large area high grayscale emphasis, normalized grayscale non-uniformity, normalized region size non-uniformity, region percentage and large area emphasis.

[0011] Furthermore, the clinical data and the imaging group characteristic data are input into the risk prediction model to calculate the risk value, including: obtaining the neurovascular compression degree value, neurovascular compression site weight value, vertical height value of the trigeminal nerve cistern segment, increased cross-sectional area value of the trigeminal nerve cistern segment and nerve-pons angle value and the first score, second score, third score, fourth score, fifth score and sixth score corresponding to the imaging group characteristic data, as well as the corresponding total score based on the nomogram model.

[0012] Furthermore, the sixth score corresponding to the characteristic data of the image group is obtained, including: obtaining the first-order minimum value, the first-order tenth percentile, the first-order energy value, the first-order ninetieth percentile, the first-order total energy value, the first-order interquartile range, the first-order skewness value, the first-order range, the first-order mean absolute deviation, the neighborhood grayscale difference matrix busyness, the grayscale co-occurrence matrix difference entropy, the grayscale co-occurrence matrix inverse variance, the grayscale co-occurrence matrix correlation, the grayscale co-occurrence matrix cluster prominence, the grayscale co-occurrence matrix autocorrelation, the grayscale size area matrix large area high grayscale emphasis, the grayscale size area matrix normalized grayscale unevenness, the grayscale size area matrix normalized area size unevenness, the grayscale size area matrix area percentage, the grayscale size area matrix large area emphasis and the grayscale correlation matrix dependent variance corresponding to the sub-scores, and updating the sub-scores according to the calculated weights corresponding to the above data and weighting them to obtain the seventh score.

[0013] In a second aspect, a trigeminal neuralgia risk prediction system is provided, the system comprising: a data acquisition unit for acquiring clinical data of a patient to be diagnosed and imaging group characteristic data of a target area of ​​the patient to be diagnosed; the clinical data are the neurovascular compression degree value, the neurovascular compression site weight value, the vertical height value of the trigeminal nerve cistern segment, the enlarged cross-sectional area value of the trigeminal nerve cistern segment and the nerve-pons angle value; a risk prediction unit for inputting the clinical data and the imaging group characteristic data into a risk prediction model to calculate the risk value.

[0014] Furthermore, the risk prediction module includes a first score calculation unit, a second score calculation unit, a third score calculation unit, a fourth score calculation unit, a fifth score calculation unit and a sixth score calculation unit.

[0015] Furthermore, the first score calculation unit is used to obtain a first score corresponding to the degree of neurovascular compression; the second score calculation unit is used to obtain a second score corresponding to the weight value of the neurovascular compression site; the third score calculation unit is used to obtain a third score corresponding to the vertical height value of the trigeminal nerve cistern segment; the fourth score calculation unit is used to obtain a fourth score corresponding to the increased cross-sectional area value of the trigeminal nerve cistern segment; the fifth score calculation unit is used to obtain a fifth score corresponding to the nerve-pons angle value; and the sixth score calculation unit is used to obtain a sixth score corresponding to the characteristic data of the imaging group.

[0016] In the technical solution provided in the embodiments of the present application, trigeminal neuralgia can be effectively evaluated by combining multiple groups of indicators and imaging group feature data, and score data reflecting trigeminal neuralgia can be obtained. The probability and severity of trigeminal neuralgia can be determined more intuitively through this score data, thereby realizing overall automated prediction of trigeminal neuralgia. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] The methods, systems, and / or programs in the accompanying drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example numerals represent similar structures in the various views of the drawings.

[0019] Figure 1 This is a flow chart of the method for predicting the risk of trigeminal neuralgia provided in an embodiment of the present application.

[0020] Figure 2 This is a schematic diagram of the ROI segmentation result provided in an embodiment of the present application.

[0021] Figure 3 It is a schematic diagram of the nomogram model in the embodiment of the present application.

[0022] Figure 4 It is a schematic diagram of the system structure provided in an embodiment of the present application.

[0023] Figure 5 This is a schematic diagram of the terminal device structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0025] In the following detailed description, numerous specific details are set forth by way of example in order to provide a thorough understanding of the relevant teachings. However, it will be apparent to one skilled in the art that the present application can be practiced without these details. In other instances, well-known methods, procedures, systems, compositions, and / or circuits have been described at a relatively high level, without detail, to avoid unnecessarily obscuring aspects of the present application.

[0026] Flowcharts are used in this application to illustrate the execution processes performed by the system according to the embodiments of the present application. It should be clearly understood that the execution processes of the flowcharts may not be executed in sequence. Instead, these execution processes may be executed in reverse order or simultaneously. In addition, at least one additional execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0027] Before further explaining the embodiments of the present invention in detail, the nouns and terms involved in the embodiments of the present invention are explained. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.

[0028] (1) In response to, it is used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0029] (2) Based on, used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0030] Primary trigeminal neuralgia (PTN) is a common neuropathic pain disorder characterized by recurrent, severe pain in the trigeminal nerve distribution area on the affected side of the face. The prevalence of trigeminal neuralgia is approximately 0.16% to 0.3%, with an annual incidence of approximately 12.6 to 27.0 cases per 100,000 people. The incidence rate in women is three times higher than in men, and the average age of onset is generally between 53 and 57 years.

[0031] PTN accounts for 75% of all trigeminal neuralgia (TN) cases. Compared to secondary TN, the etiology of PTN is not fully understood, and specific diagnostic methods are lacking, making diagnosis extremely challenging. Secondary TN, on the other hand, is often accompanied by other well-recognized secondary factors such as tumors, inflammation, multiple sclerosis, and viral infections, making diagnosis relatively straightforward. The currently prevailing neurovascular compression (NVC) theory does not fully explain PTN, as not all PTN patients demonstrate clear imaging findings. Therefore, finding an objective, quantitative, and accurate method for diagnosing TN is crucial.

[0032] Based on the above technical problems, an embodiment of the present application provides a method for predicting the risk of trigeminal neuralgia, which obtains an MRI image by performing magnetic resonance imaging on the target area of ​​the patient, and obtains multiple image group feature data by extracting and screening the image group features in the MRI image. The multiple image group feature data and multiple clinical data are processed based on a joint scoring model to obtain a risk prediction score for whether the trigeminal neuralgia disease has the risk of trigeminal neuralgia.

[0033] This embodiment provides an automated auxiliary diagnosis method for trigeminal neuralgia, which uses MRI images and multiple clinical indicators to establish a joint evaluation model. Based on this joint evaluation model, a predictive score is given for the incidence probability and severity of trigeminal neuralgia, with a high detection rate and accuracy.

[0034] For more information on this risk prediction method, see Figure 1 , including the following steps:

[0035] Step S11: Acquire clinical data of the patient to be diagnosed and image group feature data of the target area of ​​the patient to be diagnosed.

[0036] In this embodiment, this method adopts a combined prediction method to predict trigeminal neuralgia, especially primary trigeminal neuralgia, especially to predict the probability and severity of trigeminal neuralgia. The logic of the combined prediction method is to obtain the score corresponding to each indicator based on the nomogram through the prediction model, and then obtain the total score. The total score is used to determine the possibility of trigeminal neuralgia.

[0037] The indicators in this embodiment include clinical data and MRI image group feature data, wherein the image group feature data is image feature data of a neural MRI sequence and a vascular MRI sequence, and the risk prediction model is a nomogram model.

[0038] Specifically, the clinical data in the indicators include the degree of neurovascular compression, the weight of the neurovascular compression site, the vertical height of the trigeminal nerve cistern segment, the enlarged cross-sectional area of ​​the trigeminal nerve cistern segment, and the nerve-pons angle.

[0039] Among them, the multiple clinical data in this implementation were obtained by screening clinical data that appeared in the clinic. The specific screening method was to integrate the initial clinical data, including the degree of vascular compression (divided into four levels: away from, contact, compression, and nerve pushing), the location of vascular compression or proximity to the nerve (proximal and distal), the thickness of the nerve's adjacent blood vessels, the maximum transverse diameter, maximum diameter, and maximum cross-sectional area of ​​the Merkel cavity, the maximum cross-sectional area (or thickness) of the trigeminal nerve cistern segment, the height difference between the highest and lowest points of the trigeminal nerve cistern segment, the area of ​​the prepontine cistern, APR, TPA, and ATN, a total of 13 clinical characteristics. Then, several clinical characteristics associated with symptomatic nerves were identified through univariate analysis, including the maximum cross-sectional area of ​​the trigeminal nerve cistern segment, the vertical height of the trigeminal nerve cistern segment, ATN, APR, TPA, the location of vascular compression of the nerve (adjacent), and the degree of vascular compression (P < 0.05).

[0040] The image group feature data in this embodiment includes first-order features, neighborhood grayscale difference matrix busyness, multiple grayscale co-occurrence matrix features, multiple grayscale region matrix features, and grayscale correlation matrix dependent variance.

[0041] Among them, the first-order minimum value includes the first-order minimum value, the first-order tenth percentile, the first-order energy value, the first-order ninetieth percentile, the first-order total energy value, the first-order interquartile range, the first-order skewness value, the first-order range, and the first-order mean absolute deviation; multiple grayscale co-occurrence matrix features include differential entropy, inverse variance, correlation, cluster prominence, and autocorrelation; multiple grayscale size area matrix features include large area high grayscale emphasis, normalized grayscale inhomogeneity, normalized area size inhomogeneity, area percentage, and large area emphasis.

[0042] Among them, the acquisition of the above-mentioned imaging group feature data is first performed by image segmentation and region of interest extraction on the MRI image. Specifically, the ROI range of the trigeminal nerve is selected from the point where the TN enters the pons to the entrance of the Meckel cavity. Use ITK-SNAP to manually segment the ROI in the neural MRI sequence and vascular MRI sequence images layer by layer; finally, the segmentation data (mask data) of the patient examination is processed, and only the ROI on the symptomatic side is retained for TN patients. Among them, for the segmentation results of the ROI area, please refer to Figure 2 As shown, Figure 2 A is the left ROI and right ROI of the MRI nerve sequence, Figure 2 B shows the left ROI and right ROI of the MRI vascular sequence.

[0043] During the feature extraction process, DICOM images were resampled with the same pixel spacing (0.5x0.5x0.5mm). Quantile normalization was used to normalize the pixel intensity of neural and vascular MRI sequences from 0 to 512.

[0044] The radiomics toolkit was used to extract the radiomics features of each ROI, and LASSO regression was used to reduce the dimensionality of the features. Radiomics features with a weight of 0 were removed to reduce overfitting caused by high-dimensional data.

[0045] The above method is used to finally screen out multiple image group feature data and multiple clinical data in this embodiment.

[0046] Step S12: Input the clinical data and the imaging group feature data into a risk prediction model to calculate the risk value.

[0047] In this embodiment, the risk prediction model is a nomogram model. The nomogram model obtains the mapping scores corresponding to the multiple clinical data and imaging group characteristic data obtained in step S11, and obtains a total score based on the scores corresponding to each indicator and imaging group characteristic data. This total score is the risk value in this embodiment, which is used to characterize the grade corresponding to the onset of trigeminal neuralgia and the degree of risk of onset.

[0048] Specifically, the neurovascular compression degree value, the neurovascular compression site weight value, the vertical height value of the trigeminal nerve cistern segment, the increased cross-sectional area value of the trigeminal nerve cistern segment and the nerve-pons angle value and the first score, second score, third score, fourth score, fifth score and sixth score corresponding to the imaging group feature data are obtained respectively, and the total score is obtained based on the first score, second score, third score, fourth score, fifth score and sixth score.

[0049] For the calculation results of this nomogram model, please refer to Figure 3, where radsocre is the overall calculated score of the image group feature data, that is, the sixth score value. The calculation of the radsocre score is based on the following formula: Radscore = -0.46*first-order minimum value -0.0008*first-order tenth percentile -0.4*first-order energy value -0.02*first-order ninetieth percentile +8.32*first-order total energy value -0.000082*first-order interquartile range -0.000051*first-order skewness value -1.25*first-order range +0.0036*first-order mean absolute deviation +0.000063*neighborhood grayscale difference matrix busyness +0.000091*grayscale co-occurrence matrix difference entropy +1.5650*grayscale Degree co-occurrence matrix inverse variance +1.4762*Gray-level co-occurrence matrix correlation +0.377*Gray-level co-occurrence matrix cluster prominence +2.3012*Gray-level co-occurrence matrix autocorrelation +0.4389*Gray-level size area matrix large area high gray-level emphasis +0.1537*Gray-level size area matrix normalized gray-level unevenness +0.00794*Gray-level size area matrix normalized area size unevenness +0.1253*Gray-level size area matrix area percentage +0.2699*Gray-level size area matrix large area emphasis +0.0055*Gray-level correlation matrix dependent variance.

[0050] The specific calculation process of the nomogram in this embodiment can be directly referred to Figure 3 As shown, this can be achieved by using the calculation logic of the nomogram, which will not be described in detail in this embodiment.

[0051] In summary, in this embodiment, by combining the above-mentioned multiple groups of indicators, the possibility and severity of trigeminal neuralgia can be effectively evaluated in advance, and score data reflecting trigeminal neuralgia can be obtained. The probability and severity of trigeminal neuralgia can be determined more intuitively through this score data, thereby realizing the overall automated prediction of trigeminal neuralgia.

[0052] See Figure 4 With respect to the method provided in step S11-step S12, this embodiment further provides a trigeminal disease risk prediction system 40, the system comprising:

[0053] A data acquisition unit 41 is used to acquire clinical data of a patient to be diagnosed and image group feature data of a target area of ​​the patient to be diagnosed;

[0054] The risk prediction unit 42 is configured to input the clinical data and the imaging group feature data into a risk prediction model to calculate a risk value.

[0055] In this embodiment, the risk prediction model is a nomogram model.

[0056] See Figure 5The above method can also be integrated into the provided terminal device 50. In view of the fact that the device may have relatively large differences due to different configurations or performance, it can include one or more processors 501 and memory 502. The memory 502 can store one or more applications or data. The memory 502 can be a temporary storage or a persistent storage. The application stored in the memory 402 can include one or more modules (not shown in the figure), each of which can include a series of computer-executable instructions in the terminal device. Furthermore, the processor 501 can be configured to communicate with the memory 502, and the terminal device can execute the series of computer-executable instructions in the memory 502. The terminal device can also include one or more power supplies 403, one or more wired or wireless network interfaces 504, one or more input / output interfaces 505, one or more keyboards 506, etc.

[0057] In a specific embodiment, the terminal device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions in the terminal device, and the one or more programs are configured to be executed by one or more processors, including computer-executable instructions for performing the following:

[0058] Acquiring clinical data of a patient to be diagnosed and imaging feature data of a target area of ​​the patient to be diagnosed;

[0059] The clinical data and the imaging group characteristic data are input into a risk prediction model to calculate the risk value; the risk prediction model is a nomogram model.

[0060] The following is a detailed introduction to the various components of the processor:

[0061] In this embodiment, the processor is an application specific integrated circuit (ASIC), or is configured to implement one or more integrated circuits of the embodiments of the present application, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0062] Optionally, the processor can execute various functions by running or executing the software program stored in the memory and calling the data stored in the memory, such as executing the above Figure 1 The method shown.

[0063] In a specific implementation, as an embodiment, the processor may include one or more microprocessors.

[0064] The memory is used to store the software program for executing the solution of the present application, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0065] Alternatively, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processing unit through the interface circuit of the processor, and the embodiments of the present application do not specifically limit this.

[0066] It should be noted that the structure of the processor shown in this embodiment does not constitute a limitation on the device. The actual device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0067] In addition, the technical effects of the processor can refer to the technical effects of the method described in the above method embodiment, and will not be repeated here.

[0068] It should be understood that the processor in the embodiments of the present application may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0069] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0070] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (such as infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0071] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0072] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0073] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0074] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0076] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0077] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0078] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0079] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for predicting the risk of trigeminal neuralgia, characterized in that: The method comprises: Acquiring clinical data of a patient to be diagnosed and imaging feature data of a target area of ​​the patient to be diagnosed; the imaging feature data is MRI image feature data, and the clinical data is a neurovascular compression degree value, a neurovascular compression site weight value, a vertical height value of the trigeminal nerve cistern segment, an enlarged cross-sectional area value of the trigeminal nerve cistern segment, and a neuro-pons angle value; The clinical data and the imaging group characteristic data are input into a risk prediction model to calculate the risk value; the risk prediction model is a nomogram model.

2. The method for predicting the risk of trigeminal neuralgia according to claim 1, wherein: The image group feature data includes multiple first-order features, neighborhood grayscale difference matrix busyness, multiple grayscale co-occurrence matrix features, multiple grayscale region matrix features, and grayscale correlation matrix dependent variance.

3. The method for predicting the risk of trigeminal neuralgia according to claim 2, wherein: The multiple first-order features include a first-order minimum value, a first-order tenth percentile, a first-order energy value, a first-order ninetieth percentile, a first-order total energy value, a first-order interquartile range, a first-order skewness value, a first-order range, and a first-order mean absolute deviation.

4. The method for predicting the risk of trigeminal neuralgia according to claim 3, wherein: The plurality of gray-level co-occurrence matrix features include differential entropy, inverse variance, correlation, cluster prominence, and autocorrelation.

5. The method for predicting the risk of trigeminal neuralgia according to claim 4, wherein: The plurality of grayscale size region matrix features include large area high grayscale emphasis, normalized grayscale non-uniformity, normalized region size non-uniformity, region percentage, and large area emphasis.

6. The method for predicting the risk of trigeminal neuralgia according to claim 5, wherein: The inputting of the clinical data and the imaging group characteristic data into the risk prediction model to calculate the risk value includes: obtaining the neurovascular compression degree value, the neurovascular compression site weight value, the vertical height value of the trigeminal nerve cistern segment, the increased cross-sectional area value of the trigeminal nerve cistern segment and the nerve-pons angle value and the first score, second score, third score, fourth score, fifth score and sixth score corresponding to the imaging group characteristic data, as well as the corresponding total score based on the nomogram model.

7. The method for predicting the risk of trigeminal neuralgia according to claim 6, wherein: The sixth score corresponding to the characteristic data of the image group is obtained, including: obtaining the first-order minimum value, the first-order tenth percentile, the first-order energy value, the first-order ninetieth percentile, the first-order total energy value, the first-order interquartile range, the first-order skewness value, the first-order range, the first-order mean absolute deviation, the neighborhood grayscale difference matrix busyness, the grayscale co-occurrence matrix difference entropy, the grayscale co-occurrence matrix inverse variance, the grayscale co-occurrence matrix correlation, the grayscale co-occurrence matrix cluster prominence, the grayscale co-occurrence matrix autocorrelation, the grayscale size area matrix large area high grayscale emphasis, the grayscale size area matrix normalized grayscale unevenness, the grayscale size area matrix normalized area size unevenness, the grayscale size area matrix area percentage, the grayscale size area matrix large area emphasis and the grayscale correlation matrix dependent variance corresponding sub-scores, and updating the sub-scores according to the calculated weights corresponding to the above data and weighting them to obtain the seventh score.

8. A trigeminal neuralgia risk prediction system, characterized in that: The system comprises: a data acquisition unit, configured to acquire clinical data of a patient to be diagnosed and imaging feature data of a target region of the patient to be diagnosed; the clinical data comprising a neurovascular compression degree value, a neurovascular compression site weight value, a vertical height value of the trigeminal nerve cistern segment, an enlarged cross-sectional area value of the trigeminal nerve cistern segment, and a neuro-pons angle value; The risk prediction unit is used to input the clinical data and the imaging group characteristic data into a risk prediction model to calculate the risk value.

9. The trigeminal neuralgia risk prediction system according to claim 8, characterized in that: The risk prediction module includes a first score calculation unit, a second score calculation unit, a third score calculation unit, a fourth score calculation unit, a fifth score calculation unit, and a sixth score calculation unit.

10. The trigeminal neuralgia risk prediction system according to claim 9, characterized in that: The first score calculation unit is used to obtain a first score corresponding to the neurovascular compression degree value; the second score calculation unit is used to obtain a second score corresponding to the neurovascular compression site weight value; the third score calculation unit is used to obtain a third score corresponding to the vertical height value of the trigeminal nerve cistern segment; the fourth score calculation unit is used to obtain a fourth score corresponding to the increased cross-sectional area value of the trigeminal nerve cistern segment; and the fifth score calculation unit is used to obtain a fifth score corresponding to the nerve-pons angle value; The sixth score calculation unit is configured to obtain a sixth score corresponding to the image group feature data.

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