Human hair detection method, digital diagnosis and treatment method, system, device and medium for androgenic alopecia

Abstract

The present invention discloses a human hair detection method, digital diagnosis and treatment method, system, equipment and medium for androgenic alopecia, which are applied to the field of artificial intelligence medical technology. The present invention addresses the problem that the existing technology can only detect the health status of hair but cannot provide preliminary diagnosis and treatment opinions. The present invention constructs a hair status detection neural network model based on adaptive cropping and multi-granularity feature extraction to accurately detect hair images. The hair status detection results are combined with quantitative data from a special disease scale questionnaire to generate a decision tree model for hair classification, thereby outputting system diagnosis opinions or intervention opinions.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence medical technology, and in particular relates to a hair loss detection technology, diagnosis and treatment technology and system. Background Art

[0002] Androgenic alopecia is a common hair disorder affecting a wide range of men and women. While various treatments are currently available for androgenic alopecia, including medication, lifestyle adjustments, and hair follicle transplantation, each has its limitations. For example, medication treatments are long and slow to take effect, often leading to low patient compliance—meaning, when patients undergo medical intervention, they rarely follow the advice or instructions of medical professionals to implement their treatment plans. Lifestyle adjustments also lack effective guidance and oversight, making it difficult to achieve effective treatment results. Hair follicle transplantation surgery is expensive, has a long recovery period, and requires specific criteria for the patient's baldness type. Existing hair loss treatments lack personalized and dynamic monitoring capabilities. With the advancement of information technology, some mobile-based hair health assessment tools have emerged, but these tools typically only provide basic health advice and lack in-depth medical assessments and personalized intervention plans.

[0003] Existing technologies for hair health testing fall into two main categories: one uses high-speed cameras to take photos of hair, which are then evaluated by online doctors; the other involves developing hardware devices that are worn over a long period of time in the testing area and use optical sensors to track and monitor hair health. After the test is complete, the tracking data stored in the device must be exported for further analysis. These two methods merely measure hair health and cannot provide a quick, preliminary diagnosis. Furthermore, these systems require numerous external components to complete the test, significantly inconvenient for users. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a digital diagnosis and treatment method, system, equipment and medium for human hair for androgenic alopecia.

[0005] One of the technical solutions adopted by the present invention is: a human hair detection method for androgenic alopecia, comprising:

[0006] A1. Construct a neural network model for hair state detection based on adaptive cropping and multi-granularity feature extraction;

[0007] A2. Obtain hair images of different states and their corresponding offline medical records and clinical opinions, and annotate each hair image based on the offline medical records and clinical opinions to obtain a training dataset.

[0008] A3, using the training data set of step A2 to train the hair state detection neural network model constructed in step A1;

[0009] A4. Input the hair image to be detected into the trained hair status detection neural network model to obtain the hair status detection result.

[0010] The second technical solution adopted by the present invention is: a digital diagnosis and treatment method for human hair for androgenic alopecia, comprising:

[0011] B1. Data acquisition steps include:

[0012] B11. Using the human hair detection method for androgenic alopecia according to claim 1, obtaining a hair detection result of the hair image to be detected;

[0013] B12. Obtain data from the disease-specific questionnaire, which includes information on hair density, hair diameter, hair follicle health, genetic history, dietary habits, lifestyle, stress level, and medication use.

[0014] B2. Data preprocessing steps include:

[0015] B21. Use Cronbach's coefficient to quantify the data of the disease-specific scale questionnaire to obtain quantitative data;

[0016] B22. Classify the hair detection results of step B11 and the quantitative data obtained in step B21 according to BASP, mark them as feature selection criteria, and obtain a feature data set;

[0017] B23. Calculate the information entropy of each feature in the feature data set, and calculate the information gain based on the information entropy;

[0018] B3. Data analysis step: providing a basis for decision tree generation for the C4.5 decision tree algorithm through information gain, thereby generating a diagnosis and treatment opinion and intervention opinion decision tree with different intervention levels for different types and severity of hair conditions.

[0019] The third technical solution adopted by the present invention is: a human hair digital diagnosis and treatment system for androgenic alopecia, including: a data processing module, and an information acquisition module electrically connected to the data processing module; the information acquisition module is used to obtain hair images and special disease scale questionnaire information; the data processing model outputs medical advice or intervention advice based on the hair images and special disease scale questionnaire information.

[0020] The fourth technical solution adopted by the present invention is: a computer device, comprising: a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a digital diagnosis and treatment method for human hair for androgenic alopecia.

[0021] The fifth technical solution adopted by the present invention is: a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a digital diagnosis and treatment method for human hair for androgenic alopecia.

[0022] Beneficial effects of the present invention: The present invention constructs a neural network model for hair status detection based on adaptive cropping and multi-granularity feature extraction. Compared with the existing technology, it can more efficiently crop out useless areas in hair images, improve the ability to extract features of different granularities, and ultimately effectively improve the detection accuracy of hair images; and combines hair status data with quantitative data from a special disease scale questionnaire to generate a decision tree for hair classification. Compared with the existing technology, the present invention can comprehensively consider the hair status predicted based on images and the special disease scale questionnaire in text form during the digital diagnosis and treatment of hair. The content of the questionnaire includes hair density, hair diameter, hair follicle health, genetic history, eating habits, living habits, mental stress level, and medication status, etc., thereby improving the auxiliary diagnosis capability of digital diagnosis and treatment, and outputting system medical advice or intervention advice. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of the human hair health detection structure provided by an embodiment of the present invention;

[0024] Figure 2 A diagram showing the structure of a human hair health detection network model provided by an embodiment of the present invention;

[0025] Figure 3 The human hair digital diagnosis and treatment system architecture provided by the embodiment of the present invention;

[0026] Figure 4 A schematic diagram of the functional modules of the human hair digital diagnosis and treatment system provided by an embodiment of the present invention;

[0027] Figure 5 A system block diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0028] To facilitate those skilled in the art to understand the technical content of the present invention, the present invention is further explained below with reference to the accompanying drawings.

[0029] like Figure 1 As shown, the present invention provides a digital diagnosis and treatment method for human hair for androgenic alopecia, comprising the following steps:

[0030] Step 1: Prepare 1,000 hair image samples from different patients and 1,000 corresponding offline medical records and clinical opinions, as well as 1,000 questionnaires at three time points after the diagnosis of the image samples.

[0031] like Figure 2 The figure shows the structure of the human hair health detection network model of the present invention. The detection network is based on adaptive cropping and multi-granularity feature extraction, using ResNet-50 as the backbone network. An adaptive cropping module is added before the first residual block of ResNet-50 to achieve automatic cropping of hair regions. The adaptive cropping module consists of a 3*3 convolutional layer, group normalization, a ReLU activation layer, and a self-attention module, and is primarily used to output hair region and position mask information. The ResNet-50 network primarily consists of four residual blocks. The original image is fed into the adaptive cropping module. The output of the adaptive cropping module is multiplied with the original image and then fed into the first residual block of ResNet-50. The output of the first residual block is multiplied with the output of the adaptive cropping module and fed into the second residual block of ResNet-50. The output of the second residual block is then multiplied with the output of the adaptive cropping module and fed into the third residual block of ResNet-50. The output of the third residual block is then multiplied with the output of the adaptive cropping module and fed into the fourth residual block, thereby achieving automated cropping of hair regions. The four residual blocks in ResNet-50 output feature maps of four different sizes: 32*32, 64*64, 128*128, and 256*256, respectively. A multi-grain feature network is introduced after the ResNet-50 backbone network. The features output by the four residual blocks are input into the four reshape (reconstruction size) of the multi-grain feature network to achieve feature data conversion. The converted data is input into a fully connected layer, and finally the hair detection results are output, including average color, texture, morphology, edge length and outline, glossiness, etc.

[0032] After the detection model was constructed, 1,000 gross images of different hair conditions were collected. The hair condition type data and hair region data were annotated based on the collected offline medical records and clinical opinions. The collected data was divided into training and test sets in a ratio of 7:3 based on the number of data. The Adam optimizer and a learning rate of 0.08 were set for training. By dynamically setting the number of epochs and plotting the training and validation loss curves, the changes in training and validation loss were observed. If the validation loss of the detection model continues to decrease slightly over multiple epochs during the validation process, it indicates that there is room for improvement. The number of epochs can be adjusted appropriately until the validation loss between two iterations of the model is less than 0.0001. This indicates that the validation loss of the model is no longer improving and the detection model training can be stopped. At this point, the detection model is considered to be fully trained.

[0033] The medical information includes: the patient's specific conditions during offline medical treatment, such as the patient's low hair density and family history of the disease, the patient's lifestyle with a monotonous diet, etc., and the doctor's diagnosis based on the patient's specific conditions, such as the offline diagnosis of hereditary hair loss (androgenic alopecia); the clinical opinion refers to the treatment plan corresponding to the patient's condition, for example, the corresponding clinical opinion given in this embodiment is: adjust the diet structure and increase nutrition; take supplements rich in vitamins and minerals every day; maintain a regular work and rest schedule and reduce stress; and use drugs such as minoxidil to promote hair growth.

[0034] In this embodiment, when training the hair detection model, each hair image input is accompanied by the corresponding offline medical consultation information and corresponding text description of the clinical opinion, that is, the corresponding text description of the medical consultation information and clinical opinion is used as the annotation of the hair image; when a hair image without offline medical consultation is input into the trained hair detection model, the hair detection result output by the model will be accompanied by the matching annotation, which is specifically the text description of the matching preliminary diagnosis opinion and clinical opinion.

[0035] The detection process includes the contents of steps 2-4;

[0036] Step 2: crop the hair image to obtain the hair area and position mask information in the hair image;

[0037] Step 3: Input the hair area and position mask information in the hair image into ResNet-50 to obtain feature maps of different scales;

[0038] Step 4: Input the feature maps of different scales obtained in step 3 into the multi-grain feature network to obtain the hair detection results;

[0039] Step 5: Use Cronbach's (reliability) coefficient to quantify the correlation between the questionnaire survey results and obtain the correlation strength between the questions in the questionnaire and the types in the BASP classification system;

[0040] The correlation strength between the questions in the questionnaire and the types in the BASP classification system is obtained. The Cronbach (reliability) coefficient formula is:

[0041]

[0042] Among them, α is the reliability coefficient, K is the number of test questions, represents the score variation of all subjects on question i, is the variance of the total scores of all subjects.

[0043] Step 6: After obtaining the relevant coefficients from the questionnaire, the hair test data from step 4 and the quantitative coefficients from the questionnaire from step 5 are annotated according to the BASP classification as feature selection criteria. Feature data includes BASP classification and its level (F, M, C, U and its specific level), hair density, hair diameter, hair follicle health, genetic history, dietary habits, lifestyle, mental stress level, treatment history, etc. The information entropy, feature entropy, and information gain of the features are calculated. The lower the information entropy, the purer the data, and the greater the information gain.

[0044] Select the data with the largest information gain and the most effective feature division in the same type as the basis for feature classification and some nodes in the decision tree. Specifically:

[0045] After obtaining the information entropy, feature entropy, and information gain data, the information gain rate is obtained through information gain and information entropy. The information gain rate provides the basis for generating a decision tree for the C4.5 decision tree algorithm. A tree node is generated according to the data set of the selected features, and the recursion is repeated continuously to generate a diagnosis and treatment opinion and intervention opinion decision tree with different intervention levels for different types and severities of hair conditions.

[0046] During the decision tree generation process, the initial information entropy calculation formula is:

[0047]

[0048] where p i is the proportion of samples belonging to the (i)th category in the dataset.

[0049] The calculation formula of feature information entropy is:

[0050]

[0051] Where Values(A) represents the set of all possible values ​​of feature(A), Sv represents the number of samples in the dataset when the feature (A) takes the value (V), (|S|) is the total number of samples, and H(S v ) indicates that the sub-dataset (S) is v The information entropy of ) is calculated in the same way as the initial information entropy; the value of the feature here is the quantization result of the relevant feature in step 5, that is, the value of α. The information gain calculation formula is:

[0052] Gain(S,A)=H(S)-H(S|A)

[0053] Perform similar calculations on all features, select the features with the largest information gain as the partitioning features, and use them as some nodes in the decision tree. Through the partitioning of the decision tree, the system can output accurate diagnosis and treatment opinions and intervention opinions.

[0054] The final output of the decision tree is text content composed of multiple condition matches. The conditions here specifically refer to various features, such as: average color, texture, shape, edge length and outline, glossiness, etc., including the detection indicators in the medical record and the results of the questionnaire information survey. Each feature is accompanied by an annotation, that is, the corresponding text description of the corresponding medical information and clinical opinions. The text descriptions corresponding to multiple features are combined and output, which is the preliminary diagnosis and treatment opinions and intervention opinions for the current hair image to be tested.

[0055] For example, based on the various features contained in the test results of a hair image to be tested, the matching diagnosis is: hereditary hair loss, and the matching intervention recommendations are: adjust the diet structure to increase nutritional intake; take daily supplements rich in vitamins and minerals; maintain a regular schedule and reduce stress; and use minoxidil to promote hair growth. The system will then output the matching diagnosis and intervention recommendations.

[0056] Step 1 also includes labeling the hair in the hair image according to the type of the BASP classification system to obtain the labeling data and mask position information. In actual application, the labeling data, mask position and image will be submitted to the system. After submission, the system will automatically jump to the corresponding disease scale questionnaire survey interface. In the interactive interface, the user's physical health and mental state in the past six months, medication in the past three months, average sleep time and sleep depth in the past month, etc. are answered in the questionnaire, and submitted to the system for storage. After the system obtains the questionnaire data submitted by the user, it uses Cronbach's (reliability) coefficient to quantify the questionnaire survey results.

[0057] The system outputs diagnosis, treatment, and intervention recommendations. By uploading prepared diagnostic data and clinical opinions, the system generates a specific home care plan for the user based on offline diagnostic data and clinical opinions, and updates the plan's implementation status through specific methods. During the plan update process, the system uploads the prepared follow-up questionnaire at the planned time. The system quantifies the survey results and re-inputs them into the detection model. Through regression training, the system improves the system's diagnosis, treatment, and intervention recommendations for the user.

[0058] Users manage their personal information and related hair health issues on their mobile phones. Users take photos with their mobile phones to obtain hair images, and choose to upload the obtained hair images in the mobile phone APP. After the upload is completed, the system will generate an electronic questionnaire for a specific disease scale based on the user's status information, and trigger the mobile phone APP page to jump to the questionnaire filling page. The user fills in the information in the questionnaire according to his or her actual status and submits the completed questionnaire information to the application. The application system uses Cronbach's (reliability) coefficient to perform relevant calculations on the result data of the questionnaire information based on the submitted questionnaire information, and obtains the correlation strength corresponding to the questions in the questionnaire and the types in the BASP classification system. The obtained data and the uploaded hair images are passed into the trained detection model together. The detection model outputs the corresponding detection diagnosis opinions and intervention opinions through calculation, and transmits them to the application system for feedback to the user's mobile phone APP, and reminds the user to go for offline medical treatment according to the output opinions.

[0059] The user visits an offline clinic, receives medical records and clinical opinions, and then uploads them to the system through the mobile app. The system then feeds the hair image submitted in step 2 and the data obtained from the offline clinic into the detection model. The detection model then calculates and outputs the user's home care plan for hair and transmits it to the app. The app then provides feedback to the user, generates user-specific home care plan tasks, and provides real-time reminders about hair care.

[0060] After a home care plan is generated and updated, the system monitors the user's sleep duration, sleep quality, medication names, and duration of use via a mobile app. At the scheduled time, a follow-up questionnaire on hair health status is issued to the user, prompting the user to fill out the correct information. After the user submits the follow-up survey data, the system passes the monitored sleep and medication data, as well as the quantitative data from the follow-up questionnaire, to a detection model. The detection model then calculates the recent data against the original data and outputs updates to the home plan.

[0061] In the home care plan, there are multiple follow-up questionnaire surveys. Through multiple follow-up surveys of users, data from multiple time periods are collected to form a certain amount of data, so that the system can automatically complete the statistical analysis of hair health-related issues, form an effective data proof relationship, and display the relevant data to doctors through Echarts charts, thereby helping doctors study the relationship between the diagnosis and treatment effects of hair health problems and user medication compliance.

[0062] The human hair digital diagnosis and treatment system of the present invention operates in a B / S and C / S coexistence mode. The only difference between managers or doctors and ordinary personnel is that ordinary personnel simply use the system and do not need to perform the model training mentioned in step S3 above. The model is trained by the administrator or doctor, and the model used by ordinary users is a trained and mature model. The detailed process of the entire system is described below.

[0063] The human hair digital diagnosis and treatment system architecture of the present invention is shown in FIG. Figure 3 Shown, including:

[0064] Runtime Environment: This system runs on a server running the Linux operating system. The Docker application container engine serves as the runtime environment for the diagnostic and treatment system and its dependencies. Nginx is used as the HTTP and reverse proxy web server, and WSGI is the web server gateway interface. Git is used for system code version updates, maintenance, and automated deployment.

[0065] After training the detection model, it is deployed in the prepared runtime environment and communicated with the application through a message queue, reducing the coupling between the two and improving the performance of the system and the detection model. When a user performs a hair test, they upload images and electronic questionnaire data from a disease-specific scale to the application through the app. When the application stores the user-submitted data, it also pushes the data to the message queue. The detection model extracts data for hair testing by detecting changes in the message queue data. The final output of the detection module, the system's medical advice or intervention advice, is transmitted to the application via a message queue. The application stores the data and notifies the user to view the medical advice or intervention advice.

[0066] Data Layer: The data layer primarily uses the relational database MySQL as its data storage system, while Redis is incorporated into the system as a data cache and message queue tool. MySQL primarily stores basic user information, images and feature data required for testing, offline user diagnostic data and clinical opinions, system test results, home care plan information, and related scheduled task information. Redis's functions as a data cache and message queue primarily accelerate data access, reduce database load, and, as a message queue, enable loose coupling between applications and testing models, thereby improving system stability and reliability.

[0067] Business logic layer: This detection system uses Django as the program framework for developing back-end business logic functions. At the same time, the detection model is set up independently in an environment, and the Web front-end and business logic functions are relatively separated from the inspection model. The two services communicate and execute in a message queue-triggered manner, thereby improving the scalability and reusability of the system.

[0068] View Interaction Layer: This layer directly interacts with users and consists of two main parts. The first is the middleware management interface based on a B / S architecture, primarily implemented using the ReactJS library and the Antd front-end UI component library. Cornerstone is also introduced into the detection model training interface to enable efficient annotation of submitted sample images. The second is the user app interface based on a C / S architecture, primarily implemented using native Android and iOS.

[0069] The functional modules of the human hair digital diagnosis and treatment system of the present invention are shown in FIG. Figure 4 As shown, it includes a user login module, a user management module, a disease scale management module, a hair health detection module, a diagnostic information management module, a file management module, a task management module, a data statistical analysis module, and a follow-up record management module. The usage process of the entire system is mainly divided into two types. First, the system administrator or doctor needs to participate in the above-mentioned step S1 to complete the training of the detection model and form a complete training set, so as to obtain a model that can be used by users; second, the user needs to participate in the above steps S2-S5, from submitting hair pictures to participating in follow-up surveys, providing more data to the system, so that the system can statistically analyze and change the effective correlation intensity coefficient from the data, which is convenient for doctors to study hair health.

[0070] Login module: The login module in the Hair Health Detection System mainly provides login for PC users and APP users. Users who log in from the APP are defaulted to ordinary users by the system, and users who log in from the PC are defaulted to administrators and doctors. There are three main roles in this system: ordinary users, administrators, and doctors.

[0071] User management module: There are three types of personnel in this system, so personnel with administrator roles are required to effectively manage the personnel in the entire system to meet the needs of system operation.

[0072] Special disease scale management module: Special disease scale management mainly involves operations such as querying, adding, updating, deleting test questions related to the questionnaire survey in the system, and user submission of questionnaires. It also sets the binding coefficient for each questionnaire survey item to the corresponding type in the BASP system classification, which facilitates the quantitative processing of the submitted questionnaire data and the next step of execution.

[0073] Hair Health Detection Module: This module primarily involves training the hair health detection network model and performing hair health detection. The training branch of the hair health detection network model is only accessible to doctors and administrators and is used to train the detection model. The hair health detection module for general users uses a mature detection model trained by doctors or administrators. Simply transfer the user's submitted data to the detection model, and the model will output post-test medical advice and intervention recommendations based on the data match.

[0074] Diagnostic information management module: used to store the medical information generated after the user obtains medical data and clinical opinions obtained after offline medical treatment through the file management module, so as to facilitate the user to manage personal diagnostic information. At the same time, the system will also generate a home care plan for the user based on the diagnostic information and generate corresponding scheduled tasks.

[0075] Task management module: The home care plan generated by the system for the user will be recorded in the task module at each step, and the user will be reminded to execute the plan in a timely manner through the mobile phone. The user can also choose to execute the tasks in his or her planned task list according to his or her own situation, and can choose to close the tasks that are not to be executed.

[0076] Follow-up record management module: The system generates a follow-up survey form for each ordinary user at a specified time. Each follow-up survey will be recorded in the system. Users can also obtain a list of follow-up survey record information in which they participated on the APP and view the details.

[0077] Data statistics module: The data statistics module is only developed for administrators and doctors. It mainly collects statistics on hair health test data, diagnosis data, nursing plan status and follow-up survey data submitted by users of this system, obtains a series of data through analysis, and displays the relevant analysis data to doctors, so that doctors can study the relationship between hair health, treatment methods and related influencing factors.

[0078] The present invention also provides a human hair digital diagnosis and treatment system for androgenic alopecia, such as Figure 5 As shown, it includes: a data processing module and an information acquisition module electrically connected to the data processing module; the information acquisition module is used to obtain hair images and special disease scale questionnaire information; the data processing model outputs medical advice or intervention advice based on the hair images and special disease scale questionnaire information.

[0079] The present invention also provides a computer device comprising: a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a digital diagnosis and treatment method for human hair for androgenic alopecia.

[0080] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of a digital diagnosis and treatment method for human hair for androgenic alopecia.

[0081] Those skilled in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the principles of the present invention, and it should be understood that the scope of the present invention is not limited to such specific descriptions and embodiments. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims.

Claims

1. A human hair detection method for androgenic alopecia, characterized in that: include: A1. Construct a neural network model for hair state detection based on adaptive cropping and multi-granularity feature extraction; The hair status detection neural network model structure includes: The ResNet-50 network is used as the backbone network. The ResNet-50 network consists of four residual blocks and an adaptive cropping module is added before the first residual block to achieve automatic cropping of the hair area. It also includes a multi-grain feature network. The adaptive cropping module includes: a 3*3 convolutional layer, group normalization, ReLU activation layer and a self-attention module; it is used to output hair area and position mask information; The original image is input into the adaptive cropping module. The image output by the adaptive cropping module is multiplied with the original image and then input into the first residual module. The image output by the first residual block is multiplied with the image output by the adaptive cropping module as the input of the second residual block. The image output by the second residual block is multiplied with the image output by the adaptive cropping module as the input of the third residual block. The image output by the third residual block is multiplied with the image output by the adaptive cropping module as the input of the fourth residual block. Thus, the four residual blocks output four feature maps of different sizes respectively. The multi-grain feature network includes four size transformation modules. The image output by the fourth residual block is used as the input of the first size transformation module; the image output by the third residual block and the image output by the first size transformation module are used as the input of the second size transformation module; the image output by the second residual block and the image output by the second size transformation module are used as the input of the third size transformation module; the image output by the first residual block and the image output by the third size transformation module are used as the input of the fourth size transformation module. The images output by the four size transformation modules are input into a fully connected layer, which outputs the final hair detection results. A2. Prepare 1,000 hair image samples from different patients, along with 1,000 corresponding offline medical records and clinical opinions, and 1,000 questionnaires collected at three time points after diagnosis. Obtain hair images of different states and their corresponding offline medical records and clinical opinions, and annotate each hair image based on the offline medical records and clinical opinions to obtain a training dataset. A3, using the training data set of step A2 to train the hair state detection neural network model constructed in step A1; A4. Inputting the hair image to be detected into the trained hair status detection neural network model to obtain a hair status detection result; A5. Input the questionnaires of the 1000 image samples at three time points after diagnosis in step A2 into the trained neural network model for hair status detection.

2. A human hair detection method for androgenic alopecia according to claim 1, characterized in that: The hair detection results include at least: average color, texture, shape, edge length and outline, and glossiness.

3. A digital diagnosis and treatment method for human hair in androgenic alopecia, characterized in that: include: B1. Data acquisition steps include: B11. Using the human hair detection method for androgenic alopecia according to claim 1, obtaining a hair detection result of the hair image to be detected; B12. Obtain data from the disease-specific questionnaire, which includes information on hair density, hair diameter, hair follicle health, genetic history, dietary habits, lifestyle, stress level, and medication use. B2. Data preprocessing steps include: B21. Use Cronbach's coefficient to quantify the data of the disease-specific scale questionnaire to obtain quantitative data; B22. Classify the hair detection results of step B11 and the quantitative data obtained in step B21 according to BASP, mark them as feature selection criteria, and obtain a feature data set; B23. Calculate the information entropy of each feature in the feature data set, and calculate the information gain based on the information entropy; B3. Data analysis step: providing a basis for decision tree generation for the C4.5 decision tree algorithm through information gain, thereby generating a diagnosis and treatment opinion and intervention opinion decision tree with different intervention levels for different types and severity of hair conditions.

4. A digital diagnosis and treatment method for human hair in androgenic alopecia according to claim 3, characterized in that: During the decision tree generation process, the feature with the largest information gain in each category is selected as the partition feature.

5. The digital diagnosis and treatment method for human hair in androgenic alopecia according to claim 3, characterized in that: The content of the disease-specific scale questionnaire in step S5 includes: hair density, hair diameter, hair follicle health, genetic history, eating habits, living habits, mental stress level and medication status.

6. The digital diagnosis and treatment method for human hair in androgenic alopecia according to claim 3, characterized in that: The Cronbach's coefficient calculation formula is: Among them, α is the reliability coefficient, K is the number of test questions, represents the score variation of all subjects on question i, is the variance of the total scores of all subjects.

7. A digital hair diagnosis and treatment system for androgenic alopecia, comprising: A data processing module and an information acquisition module electrically connected to the data processing module; the information acquisition module is used to acquire hair images and disease-specific scale questionnaire information; The data processing module outputs a consultation opinion or an intervention opinion based on the diagnosis and treatment method according to claim 3 according to the hair image and the disease-specific scale questionnaire information.

8. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the diagnosis and treatment method according to claim 3.

9. A computer-readable storage medium, characterized in that A computer program is stored thereon, which implements the steps of the diagnosis and treatment method according to claim 3 when executed by a processor.

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