Type 2 diabetes mellitus cognitive hypofunction prediction system and method

Abstract

The invention belongs to the technical field of medical health, and particularly relates to a type 2 diabetes mellitus cognitive hypofunction prediction system and method. By monitoring and analyzing the cognitive function impairment condition of the type 2 diabetes patient in real time, the cognitive function decline trend can be found in time, so that a scientific basis is provided for clinical diagnosis and treatment, the safety boundary of cognitive function decline can be effectively predicted through accurate image processing and data analysis, and the cognitive function decline trend can be found in time. In addition, the method can accurately evaluate the level of cognitive hypofunction according to the diffusion tensor of cognitive function impairment, provides important reference for doctors to formulate treatment schemes, and is finally beneficial to improving the living quality of the type 2 diabetes patients and reducing the risk of cognitive impairment.

Description

Technical Field

[0001] The present invention belongs to the field of medical health technology, and specifically relates to a system and method for predicting cognitive impairment in type 2 diabetes. Background Art

[0002] With the aging of the population and changes in lifestyle, the incidence of type 2 diabetes has increased year by year. Diabetes not only affects the patient's blood sugar level, but may also cause a variety of complications, including cognitive impairment. Cognitive impairment is one of the common neurological complications in diabetic patients, which can seriously affect the patient's quality of life. Therefore, early prediction and intervention of cognitive impairment in patients with type 2 diabetes is particularly important.

[0003] At present, although existing studies have focused on the relationship between diabetes and cognitive impairment, there is a lack of effective prediction models and systems to accurately evaluate and predict the progression of cognitive impairment. The present invention proposes a new type 2 diabetes cognitive impairment prediction system and method, which aims to predict the safety boundary of cognitive impairment and determine the level of cognitive impairment by real-time monitoring and analysis of the diffusion tensor of the cognitive impairment area, thereby providing more accurate diagnosis and treatment basis for clinicians. Summary of the invention

[0004] The purpose of the present invention is to provide a system and method for predicting cognitive impairment in type 2 diabetes, which can monitor and analyze the diffusion tensor of the cognitive impairment area in real time, predict the safety boundary of cognitive impairment, and determine the level of cognitive impairment, thereby improving the quality of life of patients with type 2 diabetes.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A method for predicting cognitive impairment in type 2 diabetes, comprising:

[0007] Preprocessing the acquired brain region image data and outputting them as base image data;

[0008] Extracting features from the base image data to obtain a brain region feature image related to cognitive function;

[0009] Marking cognitive function damage areas and non-damage areas in the characteristic areas of the brain region, and monitoring the diffusion tensor of the cognitive function damage areas in real time;

[0010] Performing phased analysis on the diffusion tensor of the cognitive function damage area and synchronously outputting the cognitive function impairment state, wherein the cognitive function impairment state includes an inhibition state and an abnormal state;

[0011] In the inhibition state, it is indicated that the diffusion tensor of the cognitive impairment area is within a normal range, and the diffusion tensor within the normal range is recorded as a first conditional parameter, and a safety margin of cognitive impairment is predicted based on the first conditional parameter;

[0012] In the abnormal state, it is indicated that the diffusion tensor of the cognitive function damage area is abnormal, and is synchronously output as a second condition parameter, and the cognitive function impairment level is determined based on the second condition parameter.

[0013] In a preferred embodiment, the step of preprocessing the acquired brain region image data and outputting it as base image data comprises:

[0014] Collecting the brain region image data and adding a timestamp to obtain a sample image;

[0015] and performing denoising processing on the sample image to enhance image contrast;

[0016] The denoised sample images are standardized, the formats of all the sample images are unified, and they are synchronously recorded as base image data.

[0017] In a preferred embodiment, the step of real-time monitoring the diffusion tensor of the cognitive impairment area comprises:

[0018] Obtaining the edge curve of the cognitive function impairment area;

[0019] Constructing a virtual coordinate system by counting the coordinates of each edge corner point in the edge curve in the virtual coordinate system and recording them as reference coordinate parameters;

[0020] Calculating the damage area of ​​the cognitive function damage zone according to the reference coordinate parameters;

[0021] Constructing a monitoring period, setting a plurality of monitoring nodes within the monitoring period, and collecting the damage area of ​​the cognitive function damage zone under each of the monitoring nodes in real time;

[0022] The cognitive function damage areas under the adjacent monitoring nodes are subjected to difference processing to obtain the diffusion tensor of the cognitive function damage area.

[0023] In a preferred embodiment, the step of calculating the damage area of ​​the cognitive function damage zone according to the reference coordinate parameters comprises:

[0024] Acquiring reference coordinate parameters of the cognitive function impairment area;

[0025] Get the preset calculation function;

[0026] The reference coordinate parameters are input into a calculation function, and the output result of the calculation function is recorded as the cognitive function impairment area.

[0027] In a preferred embodiment, the step of performing phased analysis on the diffusion tensor of the cognitive impairment area and synchronously outputting the cognitive impairment state comprises:

[0028] Within the monitoring period, a plurality of equally spaced parallel periods are set;

[0029] Counting the diffusion tensor of the cognitive function impairment area in each of the parallel time periods and recording it as a state assessment parameter;

[0030] Acquire a state assessment threshold, and compare the state assessment threshold with a state assessment parameter;

[0031] When the state assessment parameter is less than the state assessment threshold, it indicates that the diffusion of the cognitive function damage area is inhibited, and the cognitive function decline state is recorded as an inhibition state;

[0032] When the state assessment threshold is greater than or equal to the state assessment threshold, it indicates that the spread of the cognitive function damage area is not suppressed, and the cognitive function impairment state is recorded as an abnormal state.

[0033] In a preferred embodiment, the step of predicting the safety margin of cognitive impairment based on the first condition parameter comprises:

[0034] Acquire the first condition parameters, and arrange the first condition parameters in a time sequence of occurrence;

[0035] Obtaining an evaluation function, and inputting the first condition parameters into the evaluation function according to the arrangement order of the first condition parameters, and recording the output result of the evaluation function as a trend condition parameter;

[0036] Acquire an allowable reduction area corresponding to the first condition parameter;

[0037] Obtaining a prediction function, and inputting the allowable decline area and the trend condition parameter into the prediction function, and recording the output result of the prediction function as a prediction safety interval;

[0038] According to the predicted safety interval, the last output node of the first condition parameter is offset to obtain a safety margin for predicting cognitive impairment by the first condition parameter.

[0039] In a preferred embodiment, after the trend condition parameter is output, it is compared with a preset standard trend value, and when the trend condition parameter exceeds the standard trend value, the prediction of the safety boundary of cognitive impairment is stopped and the inhibition state is converted to an abnormal state. Otherwise, the prediction of the safety boundary of cognitive impairment continues.

[0040] In a preferred embodiment, the step of determining the level of cognitive impairment based on the second condition parameter comprises:

[0041] Obtaining the second condition parameter;

[0042] Acquire a grading interval, wherein a plurality of grading intervals are provided, and each grading interval corresponds to a cognitive impairment level;

[0043] The second condition parameter is paired with the grading interval to match the corresponding cognitive impairment level.

[0044] The present invention also provides a type 2 diabetes cognitive impairment prediction system, using the type 2 diabetes cognitive impairment prediction method, comprising:

[0045] An image processing module, wherein the image processing module is used to pre-process the acquired brain region image data and output it as base image data;

[0046] A feature extraction module, the feature extraction module is used to extract features from the base image data to obtain a brain region feature image related to cognitive function;

[0047] A marking module, the marking module is used to mark the cognitive function damage area and the non-damage area in the brain region characteristic area, and monitor the diffusion tensor of the cognitive function damage area in real time;

[0048] A state evaluation module, the state evaluation module is used to perform phased analysis on the diffusion tensor of the cognitive function damage area and synchronously output the cognitive function decline state, wherein the cognitive function decline state includes an inhibition state and an abnormal state;

[0049] A prediction module, the prediction module being used to record the diffusion tensor within the normal range as a first condition parameter in the inhibition state, and predict a safety margin of cognitive impairment based on the first condition parameter;

[0050] An abnormality assessment module is used to output the diffusion tensor of the cognitive function damage area as a second condition parameter under the abnormal state, and determine the cognitive function impairment level according to the second condition parameter.

[0051] And, an electronic device, the electronic device comprising:

[0052] at least one processor;

[0053] and a memory communicatively coupled to the at least one processor;

[0054] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned method for predicting cognitive impairment in type 2 diabetes.

[0055] The technical effects achieved by the present invention are:

[0056] The present invention can timely discover the decline trend of cognitive function by real-time monitoring and analyzing the cognitive function impairment of patients with type 2 diabetes, thereby providing a scientific basis for clinical diagnosis and treatment. Through precise image processing and data analysis, the present invention can effectively predict the safety boundary of cognitive impairment and provide patients with personalized prevention and intervention measures. In addition, the present invention can also accurately evaluate the level of cognitive impairment based on the diffusion tensor of cognitive function impairment, providing an important reference for doctors to formulate treatment plans, and ultimately helping to improve the quality of life of patients with type 2 diabetes and reduce the risk of cognitive dysfunction. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic flow chart of the method of the present invention;

[0058] Figure 2 It is a schematic diagram of the system module of the present invention;

[0059] Figure 3 It is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] Type 2 diabetes is a common chronic metabolic disease characterized by high blood sugar levels. Long-term high blood sugar levels can affect multiple systems of the body, including cognitive function. Cognitive impairment is one of the common complications of patients with type 2 diabetes, which may affect patients' daily life and work ability. Therefore, developing an effective prediction method is of great significance for early intervention and management of cognitive impairment in patients with type 2 diabetes.

[0064] See also Figure 1 As shown, the present invention provides a method for predicting cognitive impairment in type 2 diabetes, comprising:

[0065] S1, preprocessing the acquired brain region image data and outputting it as base image data;

[0066] In this step, the cognitive function of patients with type 2 diabetes is evaluated by analyzing brain region images. T2DM patients with cognitive impairment have increased brain entropy and extensive white matter fiber damage. Therefore, through the study of brain region images, early prediction of cognitive function of T2DM patients can be made. After the brain region image acquisition is completed, a series of preprocessing operations are performed on the collected brain image data to ensure the quality and accuracy of the data. The preprocessing steps include but are not limited to denoising, standardization and normalization processing, and finally the preprocessed base image data is output to provide a basis for subsequent analysis;

[0067] Specifically, the steps of preprocessing the acquired brain region image data and outputting it as base image data include:

[0068] S101, collecting brain region image data and adding a timestamp to obtain a sample image;

[0069] S102, performing denoising processing on the sample image to enhance the image contrast;

[0070] S103, performing standardization processing on the sample images after denoising, unifying the formats of all sample images, and synchronously recording them as base image data;

[0071] In steps S101-S103, in order to ensure the quality and consistency of the brain region image data, a series of preprocessing steps need to be performed on the acquired image data so that it can be finally output as the base image data. First, the image data of the brain region is collected, such as through MRI collection, so as to capture detailed images of different areas of the brain. After the collection is completed, in order to facilitate subsequent processing and tracking, a timestamp needs to be added to each sample image. In this way, each sample image will have a unique identifier to record the time and date of its collection to ensure the traceability of the data. After that, the sample image with the timestamp will be denoised. Since various noises may be introduced during the imaging process, such as equipment noise, environmental noise, etc., Gaussian filtering, median filtering, etc. can be used to remove the noise in the image. The random noise of the image is removed while retaining the detailed information of the image. After denoising, the contrast of the image will be further enhanced, making the different tissues and structures in the sample image more clearly visible, so as to achieve a better visual effect. Finally, the sample image after denoising and contrast enhancement will be standardized. The purpose of standardization is to unify the format of all sample images to ensure that they are consistent in size, resolution, and color space, including adjusting the size of the image to meet the preset standard size, adjusting the resolution of the image to ensure that all images have the same pixel density, and converting the color space of the image to meet the needs of subsequent processing. After completing the standardization process, the processed sample image will be synchronously recorded as the base image data for subsequent analysis and research.

[0072] S2, extracting features from the base image data to obtain characteristic images of brain regions related to cognitive functions;

[0073] In this step, corresponding image processing techniques, such as edge detection and texture analysis, are used to extract features that can reflect the state of cognitive function from the preprocessed base image. For example, the brain entropy of T2DM patients increases, and there is extensive white matter fiber damage. Through feature extraction, the characteristic images of the brain area that need to be analyzed can be further extracted;

[0074] S3, marking cognitive function damage areas and non-damage areas in the characteristic areas of the brain, and monitoring the diffusion tensor of the cognitive function damage areas in real time;

[0075] In this step, by comparing and analyzing the diffusion tensors of the damaged area and the non-damaged area, the specific brain area with impaired cognitive function can be more accurately located. By using corresponding image recognition technology, for example, based on diffusion tensor imaging (DTI) technology, the characteristic image of the brain area can be further analyzed to identify the area of ​​cognitive function damage. The DTI technology can provide microstructural information about the white matter fiber bundles in the brain. By measuring the diffusion of water molecules in brain tissue, the integrity and directionality of white matter fibers can be revealed. In the area of ​​cognitive function damage, due to the damage to the white matter fibers, the diffusion pattern of water molecules will show abnormalities, which can be identified by abnormal signals in the DTI image. By real-time monitoring of the changes in these diffusion tensors and real-time monitoring of the diffusion tensors of the damaged area, the change trend of the damaged area over time can be observed, thereby providing dynamic data support for predicting cognitive function decline. In addition, by comparing the diffusion tensor images at different time points, the progression speed and degree of cognitive function damage can be evaluated, providing a scientific basis for clinical diagnosis and treatment.

[0076] Specifically, the steps of real-time monitoring the diffusion tensor of the cognitive impairment area include:

[0077] S301, obtaining the edge curve of the cognitive function damage area;

[0078] S302, constructing a virtual coordinate system by counting the coordinates of each edge corner point in the edge curve in the virtual coordinate system and recording them as reference coordinate parameters;

[0079] S303, calculating the damage area of ​​the cognitive function damage zone according to the reference coordinate parameters;

[0080] S304, constructing a monitoring period, setting multiple monitoring nodes within the monitoring period, and collecting the cognitive function damage area under each monitoring node in real time;

[0081] S305, performing difference processing on the cognitive function damage areas under adjacent monitoring nodes to obtain a diffusion tensor of the cognitive function damage area;

[0082] In steps S301-S305, when collecting the diffusion tensor of the cognitive function damage area, it is first necessary to obtain the edge curve of the cognitive function damage area and clarify the initial contour information of the cognitive function damage area. The acquisition of the edge curve can be achieved through image processing technology, such as using an edge detection algorithm to identify the boundary between the damage area and the normal tissue. Then, a virtual coordinate system needs to be constructed. In this virtual coordinate system, the coordinate positions of each edge corner point in the edge curve are statistically calculated. In this way, the coordinates of the edge corner points can be recorded and used as reference coordinate parameters. After the reference coordinate parameters, the damage area of ​​the cognitive function damage area is calculated according to the reference coordinate parameters. Then, a monitoring period needs to be constructed. Multiple monitoring nodes are synchronously set in the monitoring period, and the damage area of ​​the cognitive function damage area under each monitoring node is collected in real time. Finally, the cognitive function damage areas under adjacent monitoring nodes are subjected to difference processing, so that the diffusion tensor of the cognitive function damage area can be obtained.

[0083] S4, performing phased analysis on the diffusion tensor of the cognitive function damage area, and synchronously outputting the cognitive function decline state, wherein the cognitive function decline state includes an inhibition state and an abnormal state;

[0084] In this step, by comparing the diffusion tensors at different stages, the progress of cognitive impairment can be analyzed in stages. For example, by comparing the diffusion tensors of the continuous monitoring nodes, the diffusion speed and range changes of the cognitive impairment area over time can be observed, so as to judge the inhibition state or abnormal state of cognitive impairment. The inhibition state may indicate that the cognitive impairment progresses slowly or is temporarily stable, while the abnormal state may indicate that the cognitive impairment progresses rapidly or worsens. Through stage analysis, it can provide doctors with important information about the patient's cognitive impairment trend, which is helpful to formulate a more personalized treatment plan.

[0085] Specifically, the steps of performing phased analysis on the diffusion tensor of the cognitive impairment area and synchronously outputting the cognitive impairment state include:

[0086] S401, setting a plurality of equally spaced parallel time periods within the monitoring period;

[0087] S402, counting the diffusion tensor of the cognitive function damage area in each parallel period and recording it as a state assessment parameter;

[0088] S403, obtaining a state assessment threshold, and comparing the state assessment threshold with a state assessment parameter;

[0089] When the state assessment parameter is less than the state assessment threshold, it indicates that the spread of the cognitive function damage area is inhibited, and the cognitive function decline state is recorded as the inhibition state;

[0090] When the state assessment threshold is greater than or equal to the state assessment threshold, it indicates that the spread of the cognitive impairment area has not been suppressed, and the cognitive impairment state is recorded as an abnormal state;

[0091] In steps S401-S403, when performing a phased analysis on the cognitive function damage area of ​​the patient's brain, it is first necessary to set a plurality of equally spaced parallel time periods within the monitoring period to facilitate phased evaluation of the diffusion tensor. In each parallel time period, the diffusion tensor of the cognitive function damage area is statistically counted and recorded as a state evaluation parameter. The state evaluation parameter is used to compare with a preset state evaluation threshold to determine whether the diffusion of the cognitive function damage is suppressed. If the state evaluation parameter is greater than the state evaluation threshold, it indicates that the diffusion of the cognitive function damage is suppressed. At this time, the cognitive function impairment state is recorded as an inhibition state. On the contrary, if the state evaluation threshold is greater than or equal to the state evaluation parameter, it indicates that the diffusion of the cognitive function damage is not suppressed. At this time, the cognitive function impairment state is recorded as an abnormal state. Based on this, a phased analysis result on the patient's cognitive function impairment can be obtained, thereby helping doctors provide more accurate treatment plans for patients.

[0092] S5. In the inhibition state, the diffusion tensor of the area indicating cognitive impairment is within the normal range, and the diffusion tensor within the normal range is recorded as the first conditional parameter, and the safety margin of cognitive impairment is predicted based on the first conditional parameter;

[0093] In this step, when the patient's cognitive impairment state is in an inhibited state, a personalized treatment plan can be provided for the patient by real-time monitoring of the diffusion tensor to prevent further cognitive impairment. Here, the diffusion tensor within the normal range is recorded as the first condition parameter. In the inhibited state, a safety margin can be established by recording the diffusion tensor within the normal range. Based on the first condition parameter, the safety margin of cognitive impairment can be predicted to reflect the potential risk of cognitive impairment, thereby helping doctors evaluate the treatment effect, adjust the treatment plan in time, and ensure that patients receive the best medical care;

[0094] Specifically, the step of predicting the safety margin of cognitive impairment based on the first condition parameter includes:

[0095] S501, obtaining first condition parameters, and arranging the first condition parameters according to occurrence sequence;

[0096] S502, obtaining an evaluation function, and inputting the first condition parameter into the evaluation function according to the arrangement order of the first condition parameter, and recording the output result of the evaluation function as a trend condition parameter;

[0097] S503, obtaining an allowable reduction area corresponding to the first condition parameter;

[0098] S504, obtaining a prediction function, and inputting the allowable decline area and the trend condition parameter into the prediction function, and recording the output result of the prediction function as a prediction safety interval;

[0099] S505, performing an offset process on the last output node of the first condition parameter according to the predicted safety interval, to obtain a safety margin for the first condition parameter to predict cognitive impairment;

[0100] In steps S501-S505, after the first conditional parameter is output, it will be arranged according to the time sequence of occurrence (specifically, a timestamp can be added to the first conditional parameter in advance) to facilitate the analysis of the change trend of the diffusion tensor over time. The evaluation function will predict the trend of cognitive impairment based on the arranged first conditional parameter, and its output result is the trend conditional parameter, where the expression of the evaluation function is: In the formula, R represents the trend condition parameter, T represents the length of the parallel period, m represents the number of the first condition parameter, K j and K j-1 The first condition parameter representing the adjacent arrangement position, then, the allowable reduction area corresponding to the first condition parameter needs to be obtained. The allowable reduction area is a safety threshold determined based on clinical experience or previous research. By inputting the allowable reduction area and the trend condition parameter into the prediction function together, the predicted safety interval can be obtained, where the expression of the prediction function is: In the formula, t represents the predicted safety interval, K r Represents the allowable reduction area, K d It represents the first conditional parameter with the last ranking. The predicted safety interval reflects the possible safe time range of cognitive impairment. Finally, based on the predicted safety interval, the last output node of the first conditional parameter is offset to obtain the safety boundary of the first conditional parameter predicting cognitive impairment. Of course, after the predicted safety interval is output, it can also be offset downward to reduce the prediction error. The specific adjustment range can be adjusted according to actual needs. In this way, doctors can have a clear understanding of the patient's cognitive impairment trend and formulate or adjust the treatment plan accordingly to achieve the best treatment effect.

[0101] S6. Under abnormal conditions, it is indicated that the diffusion tensor of the cognitive function damage area is abnormal, and is synchronously output as a second condition parameter, and the cognitive function impairment level is determined based on the second condition parameter.

[0102] In this step, when the patient's cognitive impairment is in an abnormal state, the diffusion tensor of the cognitive impairment area is recorded as the second condition parameter, which reflects the severity of cognitive impairment. By analyzing the second condition parameter, the specific level of cognitive impairment can be determined, thereby providing targeted treatment measures for the patient.

[0103] Specifically, the step of determining the level of cognitive impairment according to the second condition parameter includes:

[0104] S601, obtaining a second condition parameter;

[0105] S602, obtaining a grading interval, wherein a plurality of grading intervals are provided, and each grading interval corresponds to a cognitive impairment level;

[0106] S603, pairing the second condition parameter with the grading interval to match the corresponding cognitive impairment level;

[0107] In steps S601-S603, after the second conditional parameter is output, the numerical value of the second conditional parameter is compared with the grading interval to determine the specific level of cognitive impairment. The grading interval is set based on clinical data and previous studies, and each grading interval corresponds to a different degree of cognitive impairment. By matching the second conditional parameter with the grading interval, the patient's current level of cognitive impairment can be accurately identified. For example, if the second conditional parameter falls within a lower grading interval, it indicates that the degree of cognitive impairment is relatively mild. On the contrary, if it falls within a higher grading interval, it indicates that the degree of cognitive impairment is relatively severe. After determining the level of cognitive impairment, the doctor can formulate a more accurate treatment plan for the patient based on this information, in order to improve the patient's cognitive function status.

[0108] In a preferred embodiment, the step of calculating the damage area of ​​the cognitive function damage zone according to the reference coordinate parameters includes:

[0109] Obtaining the reference coordinate parameters of the cognitive function impairment area;

[0110] Get the preset calculation function;

[0111] The reference coordinate parameters are input into the calculation function, and the output of the calculation function is recorded as the cognitive function impairment area.

[0112] In this embodiment, in order to calculate the damage area of ​​the cognitive function damage area, it is first necessary to obtain the reference coordinate parameters of the cognitive function damage area and the preset measurement function, and then input the reference coordinate parameters into the measurement function to obtain an output result, which represents the damage area of ​​the cognitive function damage area. The calculation formula of the damage area of ​​the cognitive function damage area is: In the formula, S rs represents the area of ​​cognitive impairment, n represents the number of edge corners, a i and a i+1 Both represent the horizontal coordinates of the edge corner points, b i and b i+1 Indicates the vertical coordinate of the edge corner point.

[0113] In a preferred embodiment, after the trend condition parameter is output, it is compared with a preset standard trend value, and when the trend condition parameter exceeds the standard trend value, the prediction of the safety boundary of cognitive impairment is stopped and the inhibition state is converted to an abnormal state. Otherwise, the prediction of the safety boundary of cognitive impairment continues.

[0114] In this embodiment, after the trend condition parameter is output, it will be compared and analyzed with the pre-set standard trend value to ensure whether the current trend condition parameter is within an acceptable range. If it is found during the comparison process that the trend condition parameter exceeds the range of the standard trend value, measures will be taken immediately to stop executing the prediction work on the safety boundary of cognitive impairment. At the same time, the current inhibition state will be converted to an abnormal state for further analysis and processing. On the contrary, if the trend condition parameter does not exceed the standard trend value, the system will continue to execute the prediction of the safety boundary of cognitive impairment to ensure the smooth progress of the entire process.

[0115] See also Figure 2 , a type 2 diabetes cognitive impairment prediction system, using the type 2 diabetes cognitive impairment prediction method, comprising:

[0116] An image processing module, which is used to pre-process the acquired brain region image data and output it as base image data;

[0117] A feature extraction module is used to extract features from the base image data to obtain a feature image of the brain region related to cognitive function;

[0118] A marking module, which is used to mark cognitive function damage areas and non-damage areas in the characteristic areas of the brain, and monitor the diffusion tensor of the cognitive function damage areas in real time;

[0119] A state evaluation module, which is used to perform phased analysis on the diffusion tensor of the cognitive function damage area and synchronously output the cognitive function decline state, wherein the cognitive function decline state includes an inhibition state and an abnormal state;

[0120] A prediction module, the prediction module is used to record the diffusion tensor within a normal range as a first conditional parameter in an inhibited state, and predict a safety margin of cognitive impairment based on the first conditional parameter;

[0121] The abnormality assessment module is used to output the diffusion tensor of the cognitive function damage area as a second condition parameter under an abnormal state, and determine the cognitive function impairment level according to the second condition parameter.

[0122] As mentioned above, the system includes an image processing module, a feature extraction module, a labeling module, a state assessment module, a prediction module and an abnormality assessment module. The image processing module is responsible for the preliminary preprocessing of the collected brain image data. This process includes steps such as image denoising, enhancement and standardization. The purpose is to ensure the accuracy and reliability of subsequent processing. After processing, the image processing module outputs high-quality base image data to provide a solid foundation for subsequent analysis. The feature extraction module will extract the key features in the base image data and output the brain region feature image that reflects the specific changes in the brain region. The labeling module works further on the basis of feature extraction, specifically marking the cognitive function damage area and the non-damage area in the brain region feature image. At the same time, it can also continuously track the spread of the cognitive function damage area, providing doctors and researchers with The core function of the state assessment module is to conduct a phased analysis of the diffusion tensor of the cognitive impairment area. Through this analysis, the patient's current cognitive impairment state can be output synchronously. The cognitive impairment state includes the inhibition state and the abnormal state. Each state corresponds to a different degree and characteristics of cognitive impairment. The prediction module is executed in the inhibition state, specifically recording the diffusion tensor within the normal range as the first conditional parameter. Based on the first conditional parameter, the prediction module can predict the safety boundary of cognitive impairment, thereby providing an important reference for clinical intervention. The abnormal assessment module is executed in the abnormal state, outputting the diffusion tensor of the cognitive impairment area as the second conditional parameter, and determining the specific level of cognitive impairment based on these parameters, which is of great significance for formulating personalized treatment plans and management plans.

[0123] See also Figure 3 , an electronic device, the electronic device comprising:

[0124] at least one processor;

[0125] and a memory communicatively coupled to the at least one processor;

[0126] The memory stores a computer program executable by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned method for predicting cognitive impairment in type 2 diabetes.

[0127] The processor of the electronic device mentioned above can be a central processing unit (CPU) or a graphics processing unit (GPU) for processing complex computing tasks and image data. The memory can be a random access memory (RAM) or a solid state drive (SSD) for storing program code and temporary data. The computer program contains a series of instructions, which are executed by the processor to achieve the prediction of cognitive impairment in patients with type 2 diabetes. The electronic device can also include an operator, an input device, and an output device to ensure the efficient operation of the entire prediction process. The operator of the electronic device is responsible for performing specific mathematical operations and logical operations, and input devices such as keyboards and mice allow users to interact with the system, while output devices such as displays and printers are used to display prediction results and related data.

[0128] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0129] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A method for predicting cognitive impairment in type 2 diabetes, characterized in that: include: Preprocessing the acquired brain region image data and outputting them as base image data; Extracting features from the base image data to obtain a brain region feature image related to cognitive function; Marking cognitive function damage areas and non-damage areas in the characteristic areas of the brain region, and monitoring the diffusion tensor of the cognitive function damage areas in real time; Performing phased analysis on the diffusion tensor of the cognitive function damage area and synchronously outputting the cognitive function impairment state, wherein the cognitive function impairment state includes an inhibition state and an abnormal state; In the inhibition state, it is indicated that the diffusion tensor of the cognitive impairment area is within a normal range, and the diffusion tensor within the normal range is recorded as a first conditional parameter, and a safety margin of cognitive impairment is predicted based on the first conditional parameter; In the abnormal state, it is indicated that the diffusion tensor of the cognitive function damage area is abnormal, and is synchronously output as a second condition parameter, and the cognitive function impairment level is determined based on the second condition parameter.

2. A method for predicting cognitive impairment in type 2 diabetes according to claim 1, characterized in that: The step of preprocessing the acquired brain region image data and outputting it as base image data comprises: Collecting the brain region image data and adding a timestamp to obtain a sample image; and performing denoising processing on the sample image to enhance image contrast; The denoised sample images are standardized, the formats of all the sample images are unified, and they are synchronously recorded as base image data.

3. A method for predicting cognitive impairment in type 2 diabetes according to claim 1, characterized in that: The step of real-time monitoring the diffusion tensor of the cognitive function damage area comprises: Obtaining the edge curve of the cognitive function impairment area; Constructing a virtual coordinate system by counting the coordinates of each edge corner point in the edge curve in the virtual coordinate system and recording them as reference coordinate parameters; Calculating the damage area of ​​the cognitive function damage zone according to the reference coordinate parameters; Constructing a monitoring period, setting a plurality of monitoring nodes within the monitoring period, and collecting the damage area of ​​the cognitive function damage zone under each of the monitoring nodes in real time; The cognitive function damage areas under the adjacent monitoring nodes are subjected to difference processing to obtain the diffusion tensor of the cognitive function damage area.

4. A method for predicting cognitive impairment in type 2 diabetes according to claim 3, characterized in that: The step of calculating the damage area of ​​the cognitive function damage zone according to the reference coordinate parameters comprises: Acquiring reference coordinate parameters of the cognitive function impairment area; Get the preset calculation function; The reference coordinate parameters are input into a calculation function, and the output result of the calculation function is recorded as the cognitive function impairment area.

5. A method for predicting cognitive impairment in type 2 diabetes according to claim 1, characterized in that: The step of performing phased analysis on the diffusion tensor of the cognitive impairment area and synchronously outputting the cognitive impairment state comprises: Within the monitoring period, a plurality of equally spaced parallel periods are set; Counting the diffusion tensor of the cognitive function impairment area in each of the parallel time periods and recording it as a state assessment parameter; Acquire a state assessment threshold, and compare the state assessment threshold with a state assessment parameter; When the state assessment parameter is less than the state assessment threshold, it indicates that the diffusion of the cognitive function damage area is inhibited, and the cognitive function decline state is recorded as an inhibition state; When the state assessment threshold is greater than or equal to the state assessment threshold, it indicates that the spread of the cognitive function damage area is not suppressed, and the cognitive function impairment state is recorded as an abnormal state.

6. A method for predicting cognitive impairment in type 2 diabetes according to claim 1, characterized in that: The step of predicting the safety margin of cognitive impairment based on the first condition parameter comprises: Acquire the first condition parameters, and arrange the first condition parameters in a time sequence of occurrence; Obtaining an evaluation function, and inputting the first condition parameters into the evaluation function according to the arrangement order of the first condition parameters, and recording the output result of the evaluation function as a trend condition parameter; Acquire an allowable reduction area corresponding to the first condition parameter; Obtaining a prediction function, and inputting the allowable decline area and the trend condition parameter into the prediction function, and recording the output result of the prediction function as a prediction safety interval; According to the predicted safety interval, the last output node of the first condition parameter is offset to obtain a safety margin for predicting cognitive impairment by the first condition parameter.

7. A method for predicting cognitive impairment in type 2 diabetes according to claim 5, characterized in that: After the trend condition parameter is output, it is compared with the preset standard trend value. When the trend condition parameter exceeds the standard trend value, the prediction of the safety boundary of cognitive impairment is stopped and the inhibition state is converted to an abnormal state. Otherwise, the prediction of the safety boundary of cognitive impairment continues.

8. A method for predicting cognitive impairment in type 2 diabetes according to claim 1, characterized in that: The step of determining the level of cognitive impairment according to the second condition parameter comprises: Obtaining the second condition parameter; Acquire a grading interval, wherein a plurality of grading intervals are provided, and each grading interval corresponds to a cognitive impairment level; The second condition parameter is paired with the grading interval to match the corresponding cognitive impairment level.

9. A system for predicting cognitive impairment in type 2 diabetes, characterized by: The method for predicting cognitive impairment in type 2 diabetes mellitus according to any one of claims 1 to 8 comprises: An image processing module, wherein the image processing module is used to pre-process the acquired brain region image data and output it as base image data; A feature extraction module, the feature extraction module is used to extract features from the base image data to obtain a brain region feature image related to cognitive function; A marking module, the marking module is used to mark the cognitive function damage area and the non-damage area in the brain region characteristic area, and monitor the diffusion tensor of the cognitive function damage area in real time; A state evaluation module, the state evaluation module is used to perform phased analysis on the diffusion tensor of the cognitive function damage area and synchronously output the cognitive function decline state, wherein the cognitive function decline state includes an inhibition state and an abnormal state; A prediction module, the prediction module being used to record the diffusion tensor within the normal range as a first condition parameter in the inhibition state, and predict a safety margin of cognitive impairment based on the first condition parameter; An abnormality assessment module is used to output the diffusion tensor of the cognitive function damage area as a second condition parameter under the abnormal state, and determine the cognitive function impairment level according to the second condition parameter.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting cognitive impairment in type 2 diabetes according to any one of claims 1 to 8.

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