Oral disease treatment auxiliary management system based on cloud data

Abstract

The invention relates to the technical field of oral medicine, and discloses an oral disease treatment auxiliary management system based on cloud data, and the system comprises a data collection and preprocessing module which is used for collecting oral data of a patient from various sensors and medical equipment; the data storage and management module is used for storing and managing all the collected patient data; the intelligent diagnosis and prediction module is used for analyzing oral data of the patient, automatically identifying oral diseases and predicting potential health risks; the treatment scheme recommendation and optimization module is used for providing a personalized treatment scheme for the patient according to the diagnosis result and the individual difference of the patient; the remote medical treatment and collaborative diagnosis and treatment module is used for supporting remote collaboration and data sharing among doctors, patients and medical institutions; and the health management and tracking module is used for monitoring daily behaviors and treatment effects of the patient. According to the invention, through a deep learning algorithm and a machine learning technology, oral image data can be automatically analyzed, and potential oral diseases can be identified.

Description

Technical Field

[0001] The present invention relates to the technical field of oral medicine, and in particular to an oral disease treatment auxiliary management system based on cloud data. Background Art

[0002] Oral disease is one of the most common diseases in the world. With the changes in people's lifestyles and eating habits, the incidence of oral diseases has increased year by year. Traditional oral diagnosis and treatment methods usually rely on the doctor's experience and manual examination.

[0003] Traditional oral diagnosis and treatment relies on the doctor's experience and intuitive judgment, which is easily interfered by human factors, especially in complex cases, and the accuracy of diagnosis is low. In addition, current treatment plans are usually based on a standardized approach, and individual differences between different patients are not fully considered, which may lead to unsatisfactory treatment effects or too long treatment courses. Summary of the invention

[0004] In order to make up for the above shortcomings, the present invention provides an oral disease treatment auxiliary management system based on cloud data, aiming to improve the problem that traditional oral diagnosis and treatment relies on the doctor's experience and intuitive judgment, and the diagnostic accuracy is low.

[0005] In a first aspect, the present invention provides the following technical solution, a cloud-based data-based oral disease treatment auxiliary management system, comprising: Data collection and preprocessing module, used to collect patients' oral data from various sensors and medical devices, and upload the data to the cloud platform; Data storage and management module, used to store and manage all collected patient data, and provide electronic health record EHR management functions; Intelligent diagnosis and prediction module, which is used to analyze patients' oral data, automatically identify oral diseases and predict potential health risks; The treatment plan recommendation and optimization module provides patients with personalized treatment plans based on the diagnosis results and individual differences of patients, and optimizes the treatment path in real time; Telemedicine and collaborative diagnosis and treatment module, used to support remote collaboration and data sharing between doctors, patients and medical institutions; The health management and tracking module is used to monitor patients' daily behaviors and treatment effects, and provide personalized health intervention and management plans.

[0006] Preferably, the data acquisition and preprocessing module includes: A data acquisition unit, used to obtain the patient's oral data from oral imaging equipment, smart sensors and wearable devices, the data including oral imaging data, clinical examination data and patient daily behavior data; A data preprocessing unit for denoising, enhancing, and segmenting the collected oral image data to improve data quality and facilitate subsequent analysis.

[0007] Preferably, the data storage and management module includes: A data storage unit for storing the collected patient data in a cloud database and classifying and storing it according to data types, including image data, clinical data, and behavioral data; A data security management unit for encrypting patient data and being responsible for the security of patient privacy data by setting up a permission management mechanism.

[0008] Preferably, the intelligent diagnosis and prediction module includes: A data analysis unit for preliminarily analyzing the oral data collected from patients, extracting features, and performing data cleaning; Among them, the data analysis unit cleans the collected oral images and clinical data by using the NumPy and Pandas libraries, performs image denoising and enhancement using OpenCV, standardizes the image size to 256x256 pixels, and normalizes the pixel values to the [0,1] interval; The data analysis unit also increases the diversity of data through data augmentation techniques, including rotation, flipping, and shearing, to reduce the risk of overfitting; A deep learning diagnosis unit for automatically analyzing the oral images of patients through a convolutional neural network (CNN) to generate intelligent diagnosis results; Among them, the deep learning diagnosis unit constructs a CNN model using the TensorFlow or PyTorch framework. A typical CNN architecture consists of 3 convolutional layers and 2 fully connected layers, performs non-linear mapping through the ReLU activation function. The training dataset includes at least 10,000 oral image data, and the model is optimized through transfer learning techniques to make the model accuracy reach at least 85%; During the training process, the cross-entropy loss function Cross-EntropyLoss is used to optimize the model, the Adam optimizer is used, the initial learning rate is set to 0.001, the batch size is 32, and 50 epochs are trained; A disease prediction unit that combines the patient's health history, lifestyle data, and known medical data to predict potential oral health risks through machine learning algorithms and provide early warnings; Among them, the disease prediction unit applies support vector machine (SVM), random forest (RF), or gradient boosting tree (XGBoost) algorithms to establish a prediction model based on the patient's health history and lifestyle, and calculates the risk probability of oral diseases occurring; The disease prediction unit uses a k-fold cross-validation method to optimize the model, with the goal of making the area under the AUC curve greater than 0.85; When the predicted value exceeds the set threshold, a push notification is used to remind the patient to seek medical attention and undergo an oral examination in a timely manner.

[0009] Preferably, the treatment plan recommendation and optimization module includes the following units: Personalized treatment recommendation unit, which generates personalized treatment plans based on intelligent diagnosis results, patient health status and preferences; The treatment optimization unit optimizes the treatment pathway in real time and adjusts the treatment plan according to the patient's treatment progress by applying optimization algorithms, including genetic algorithms or simulated annealing.

[0010] Preferably, the telemedicine and collaborative diagnosis and treatment module includes the following units: Remote video consultation unit, which supports real-time video communication between doctors and patients through WebRTC technology for remote diagnosis and treatment recommendations; The data sharing unit uses the FHIR standard protocol to enable doctors, patients and experts to share patients' medical data securely and in real time, and conduct collaborative diagnosis and treatment across regions and institutions.

[0011] Preferably, the health management and tracking module includes the following units: Health monitoring unit, used to collect patients' health behavior data in real time, including tooth brushing frequency and eating habits, and monitor patients' health status; The health advice generation unit generates personalized health intervention measures based on the patient's health data and feedback, including oral health advice and dietary recommendations, and pushes health reminders to patients.

[0012] In a second aspect, the present invention provides the following technical solution, a method for assisting management of oral disease treatment based on cloud data, comprising the following steps: S1. Data collection and preprocessing The patient's oral data, including oral images, clinical examination data and patient behavior data, are collected through smart toothbrushes, digital imaging devices, oral endoscope medical devices and sensors. The data are transmitted to the cloud platform through wireless communication protocols and enter the pre-processing stage. The data quality is enhanced through image denoising, enhancement and standardization processing to prepare for subsequent analysis. S2. Data storage and management The collected patient data is stored in the distributed database of the cloud platform. The data is stored in categories, including imaging data, clinical data and behavioral data. Data access is controlled through role-based permission management (RBAC). S3, Intelligent Diagnosis and Prediction Use deep learning algorithms to automatically analyze patients' oral imaging data, identify potential oral diseases and generate intelligent diagnostic results. Combined with patients' health history and lifestyle data, machine learning algorithms are used to predict potential oral disease risks and provide early warnings. S4. Treatment plan recommendation and optimization Based on the intelligent diagnosis results, a personalized treatment recommendation algorithm is used to generate a treatment plan for the patient. Combined with the patient's health status and treatment feedback, an optimization algorithm is used to optimize the treatment path, and the treatment plan is adjusted and improved in real time. S5. Telemedicine and collaborative diagnosis and treatment Through video communication technology, doctors and patients can have video consultations. Multiple medical institutions and experts can share patients' oral health data through the cloud platform for joint diagnosis, treatment and decision-making. S6. Health management and tracking Track the patient's health data over a long period of time, analyze the patient's brushing frequency, eating habits and treatment effects, generate personalized health management plans based on the analysis results, and remind patients to conduct regular oral examinations and adjust their health through push notifications.

[0013] In the third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned cloud data-based oral disease treatment auxiliary management method when executing the computer program.

[0014] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, and the computer program, when executed by a processor, implements the above-mentioned cloud data-based oral disease treatment auxiliary management method.

[0015] The present invention has the following beneficial effects: 1. In the present invention, through deep learning algorithms (such as convolutional neural networks (CNN)) and machine learning techniques, oral imaging data can be automatically analyzed to identify potential oral diseases (such as caries, periodontal disease, oral tumors, etc.). The application of artificial intelligence significantly improves the accuracy of diagnosis, reduces human errors, and helps to detect and warn of oral diseases at an early stage, thereby intervening in treatment earlier.

[0016] 2. In the present invention, the system can provide patients with personalized treatment plans through the analysis of the patient's health history, living habits and treatment feedback. Different from traditional treatment methods, personalized treatment plans are more accurate and meet the actual needs of patients, thus improving the treatment effect and reducing unnecessary medical interventions.

[0017] 3. In the present invention, through the telemedicine function supported by the cloud platform, doctors can conduct video consultations with patients, which is especially important for patients in remote areas. Multiple medical institutions and experts can conduct cross-regional and cross-institutional collaborative diagnosis and treatment through a shared data platform, thereby improving the utilization efficiency of medical resources and ensuring that patients can obtain high-quality medical services through joint consultations of multiple experts.

[0018] 4. In the present invention, an optimization algorithm (such as genetic algorithm, simulated annealing, etc.) is used to adjust the treatment path in real time to ensure that each patient receives the best treatment plan. This flexible optimization process enables the treatment plan to be dynamically adjusted as the patient's treatment progresses, further improving the effectiveness and efficiency of the treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is an overall framework diagram of an oral disease treatment auxiliary management system based on cloud data proposed by the present invention; Figure 2 This is a data collection and preprocessing module framework diagram of a cloud-based oral disease treatment auxiliary management system proposed by the present invention; Figure 3 This is a data storage and management module framework diagram of a cloud-based oral disease treatment auxiliary management system proposed by the present invention; Figure 4 This is a framework diagram of an intelligent diagnosis and prediction module of an oral disease treatment auxiliary management system based on cloud data proposed by the present invention; Figure 5 This is a framework diagram of a treatment plan recommendation and optimization module of an oral disease treatment auxiliary management system based on cloud data proposed by the present invention; Figure 6 This is a framework diagram of the telemedicine and collaborative diagnosis and treatment module of an oral disease treatment auxiliary management system based on cloud data proposed by the present invention; Figure 7 This is a framework diagram of the health management and tracking module of an oral disease treatment auxiliary management system based on cloud data proposed by the present invention; Figure 8 This is a flow chart of an oral disease treatment auxiliary management method based on cloud data proposed by the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1 Reference Figure 1-Figure 7 In a first embodiment of the present invention, the present invention provides an oral disease treatment auxiliary management system based on cloud data, comprising: Data collection and preprocessing module, used to collect patients' oral data from various sensors and medical devices, and upload the data to the cloud platform; Data storage and management module, used to store and manage all collected patient data, and provide electronic health record EHR management functions; Intelligent diagnosis and prediction module, which is used to analyze patients' oral data, automatically identify oral diseases and predict potential health risks; The treatment plan recommendation and optimization module provides patients with personalized treatment plans based on the diagnosis results and individual differences of patients, and optimizes the treatment path in real time; Telemedicine and collaborative diagnosis and treatment module, used to support remote collaboration and data sharing between doctors, patients and medical institutions; The health management and tracking module is used to monitor patients' daily behaviors and treatment effects, and provide personalized health intervention and management plans.

[0022] Specifically, the oral disease treatment auxiliary management system based on cloud data has significantly improved the diagnostic accuracy of oral diseases, reduced human errors, and helped to detect and intervene in diseases at an early stage through deep learning and machine learning technologies. In addition, the system can provide personalized treatment plans based on the patient's health history and living habits, and dynamically adjust the treatment path using optimization algorithms to ensure the best treatment effect. The telemedicine function enables patients to receive expert consultations at home, which is especially important for patients in remote areas. Multiple medical institutions and experts can share data through the cloud platform for collaborative diagnosis and treatment to ensure that patients receive the best treatment plan. The system also helps patients improve their oral hygiene habits, reduce the risk of disease, and further improve health management effects by monitoring patients' oral health behaviors (such as brushing frequency and eating habits) in real time and providing personalized health management suggestions. Through the comprehensive application of these functions, the system not only improves the efficiency and quality of diagnosis and treatment of oral diseases, but also reduces medical costs, improves patients' treatment experience, and promotes the intelligent and personalized development of oral health management.

[0023] The data acquisition and preprocessing modules include: The data acquisition unit is used to obtain the patient's oral data from oral imaging equipment, smart sensors and wearable devices. The data includes oral imaging data, clinical examination data and patient daily behavior data; The data preprocessing unit is used to perform denoising, enhancement and segmentation processing on the collected oral image data to improve the data quality and facilitate subsequent analysis.

[0024] Specifically, data collection: This module collects oral data through multiple devices, including but not limited to smart toothbrushes, digital imaging devices, oral endoscopes, smart tooth sensors, etc. Each device transmits data to the cloud platform in real time through wireless communication protocols such as Bluetooth and Wi-Fi. Specifically, the smart toothbrush can record the patient's brushing frequency, time and intensity, the digital imaging device obtains high-definition images of the inside of the mouth, and the oral endoscope is used to collect more subtle images or video information.

[0025] Data preprocessing: The data obtained from different devices are of various types and must be preprocessed to ensure their consistency and usability. Image data is denoised and enhanced through image processing libraries such as OpenCV, such as adjusting brightness and contrast, and applying edge detection algorithms to improve the identifiability of key information in the image. For clinical data and patient behavior data, Python's Pandas library is used for data cleaning, processing missing values, outliers, and data standardization to ensure the accuracy of subsequent analysis.

[0026] The data storage and management modules include: A data storage unit, used to store the collected patient data in a cloud database and classify and store them according to data types, including imaging data, clinical data, and behavioral data; The data security management unit is used to encrypt patient data and is responsible for the security of patient privacy data by setting up a permission management mechanism.

[0027] Specifically, data storage: All collected oral data, including imaging data, clinical data, health history, living habits, etc., will be stored in the cloud platform and managed using a distributed database. Different types of data (such as structured data and unstructured data) will be stored in relational databases (such as MySQL, PostgreSQL) and object storage systems (such as AWS S3, Google Cloud Storage). Imaging data will be stored in a high-quality compressed format to reduce storage costs and ensure image clarity.

[0028] Data Security: Data storage is protected by encryption technology, and patient sensitive information is encrypted using advanced encryption standards such as AES-256 to ensure data security. Data access control uses the RBAC (role-based access control) mechanism to control the reading and modification permissions of data based on user identities (such as doctors, patients, and administrators), ensuring that different roles can only access authorized data.

[0029] Intelligent diagnosis and prediction modules include: A data analysis unit, used to perform preliminary analysis on the oral data collected from the patient, extract features and perform data cleaning; The data analysis unit uses libraries such as NumPy and Pandas to clean the collected oral images and clinical data to ensure data consistency, and uses OpenCV for image denoising and enhancement. The standardized image size is 256x256 pixels, and the pixel values ​​are normalized to the [0,1] interval. The data analysis unit also increases the diversity of data through data enhancement techniques (such as rotation, flipping, shearing, etc.) to reduce the risk of overfitting.

[0030] A deep learning diagnostic unit, which is used to automatically analyze the patient's oral images through a convolutional neural network (CNN) to generate intelligent diagnostic results; The deep learning diagnosis unit uses the TensorFlow or PyTorch framework to build a CNN model. The typical CNN architecture consists of three convolutional layers and two fully connected layers. Nonlinear mapping is performed through the ReLU activation function. The training data set includes at least 10,000 oral imaging data. The model is optimized through transfer learning technology to ensure that the model accuracy reaches at least 85%. During the training process, the cross-entropy loss function (Cross-EntropyLoss) was used for model optimization, the Adam optimizer was used, the initial learning rate was set to 0.001, the batch size was 32, and 50 epochs were trained.

[0031] The disease prediction unit combines the patient's health history, lifestyle data and known medical data to predict potential oral health risks through machine learning algorithms and provide early warnings; The disease prediction unit uses algorithms such as support vector machine (SVM), random forest (RF) or gradient boosting tree (XGBoost) to establish a prediction model based on the patient's health history (such as age, gender, eating habits, etc.) and lifestyle (such as brushing frequency, smoking, etc.) to calculate the risk probability of oral diseases. The disease prediction unit uses the k-fold cross-validation method to optimize the model, with the goal of ensuring that the AUC (area under the curve) value is greater than 0.85; When the predicted value exceeds the set threshold (such as 30%), the system will push notifications to remind patients to seek medical attention and undergo oral examinations in a timely manner.

[0032] Specifically, intelligent diagnosis: This module uses deep learning algorithms such as convolutional neural networks (CNN) to automatically analyze oral imaging data uploaded by patients and identify various potential oral diseases, such as caries, periodontal disease, oral tumors, etc. During the training process, labeled image datasets from multiple dental hospitals are used to ensure the diversity and representativeness of the training data. Data enhancement techniques (such as image flipping, rotation, scaling, etc.) are used to increase the diversity of the dataset and further improve the accuracy of the model.

[0033] Disease prediction: In addition to imaging data, the system also predicts the patient's oral health by analyzing the patient's health history, lifestyle and other data, combined with machine learning algorithms (such as random forests, support vector machines (SVMs), etc.). For example, the system will calculate the patient's future risk of developing dental caries, periodontal disease and other diseases based on information such as the patient's brushing habits, eating habits, smoking, and family medical history. During the training process, historical medical records are used as input to train the model to predict the probability of disease occurrence. The risk prediction value output by the model is the probability of disease occurrence, and the system will automatically generate health reminders or warnings.

[0034] The treatment plan recommendation and optimization module includes the following units: Personalized treatment recommendation unit, which generates personalized treatment plans based on intelligent diagnosis results, patient health status and preferences; The treatment optimization unit optimizes the treatment pathway in real time and adjusts the treatment plan according to the patient's treatment progress by applying optimization algorithms, including genetic algorithms or simulated annealing.

[0035] Specific, individualized treatment recommendations: By analyzing the patient's diagnosis results, the system recommends personalized treatment plans for the patient. The treatment plan will be customized based on the patient's disease type, health status, age, gender, past medical history and other factors. For example, if the system detects that the patient has caries, the treatment recommendation will include different treatment methods, such as fillings, root canal treatment, etc., and improvement suggestions will also be provided based on the patient's oral hygiene habits.

[0036] Treatment Optimization: After the patient receives treatment, the treatment path will be optimized in real time based on the patient's treatment feedback and health data. For example, during the treatment process, if the patient feedbacks that the treatment effect is not good, the system will adjust the treatment path through optimization algorithms (such as genetic algorithms or simulated annealing algorithms) and recommend new treatment methods or strategies. The system will iteratively optimize the treatment plan based on medical literature and the latest research trends.

[0037] The telemedicine and collaborative diagnosis and treatment module includes the following units: Remote video consultation unit, which supports real-time video communication between doctors and patients through WebRTC technology for remote diagnosis and treatment recommendations; The data sharing unit uses the FHIR standard protocol to enable doctors, patients and experts to share patients' medical data securely and in real time, and conduct collaborative diagnosis and treatment across regions and institutions.

[0038] Specifically, remote video consultation: This module supports real-time video communication between doctors and patients. Using WebRTC technology, doctors can conduct remote consultations with patients through the system, ensuring that patients can get timely diagnosis and treatment without having to go to the hospital in person. Through this function, patients can enjoy the diagnosis of dental experts at home, which is especially important for patients in remote areas.

[0039] Data sharing and collaborative diagnosis and treatment: Multiple medical institutions and experts can share patients' health data through the cloud platform and conduct cross-regional and cross-institutional joint consultations. All participating medical personnel can access the patient's imaging data and treatment history to provide the best treatment plan for the patient. This module realizes cross-platform and cross-system data sharing through the FHIR (Fast Healthcare Interoperability Resources) protocol to ensure data interoperability.

[0040] The health management and tracking module includes the following units: Health monitoring unit, used to collect patients' health behavior data in real time, including tooth brushing frequency and eating habits, and monitor patients' health status; The health advice generation unit generates personalized health intervention measures based on the patient's health data and feedback, including oral health advice and dietary recommendations, and pushes health reminders to patients.

[0041] Specifically, health behavior monitoring: This module collects patients' health behavior data in real time through smart devices (such as smart toothbrushes, health monitoring sensors, etc.), and monitors their brushing frequency, eating habits, oral care behaviors, etc. The system will generate personalized health reports in real time based on data changes, reminding patients to pay attention to oral health and avoid diseases.

[0042] Long-term health management: Health management is not only about diagnosis and treatment of diseases, but also about long-term health tracking and intervention. Based on the patient's health status and treatment feedback, the system will generate regular health assessment reports. The system will adjust the health management strategy based on the patient's behavioral data (such as brushing frequency, diet, oral hygiene, etc.) and push personalized health reminders (such as regular oral examinations, dietary recommendations, etc.).

[0043] Embodiment 2: Reference Figure 8 In a second embodiment of the present invention, the present invention provides an oral disease treatment auxiliary management method based on cloud data, comprising the following steps: S1. Data collection and preprocessing The patient's oral data, including oral images, clinical examination data and patient behavior data, are collected through smart toothbrushes, digital imaging devices, oral endoscope medical devices and sensors. The data is transmitted to the cloud platform through wireless communication protocols and enters the pre-processing stage. The data quality is enhanced through image denoising, enhancement and standardization processing to prepare for subsequent analysis. S2. Data storage and management The collected patient data is stored in the distributed database of the cloud platform. The data is stored in categories, including imaging data, clinical data and behavioral data. Data access is controlled through role-based permission management (RBAC). S3, Intelligent Diagnosis and Prediction Use deep learning algorithms to automatically analyze patients' oral imaging data, identify potential oral diseases and generate intelligent diagnostic results. Combined with patients' health history and lifestyle data, machine learning algorithms are used to predict potential oral disease risks and provide early warnings. S4. Treatment plan recommendation and optimization Based on the intelligent diagnosis results, a personalized treatment recommendation algorithm is used to generate a treatment plan for the patient. Combined with the patient's health status and treatment feedback, an optimization algorithm is used to optimize the treatment path, and the treatment plan is adjusted and improved in real time. S5. Telemedicine and collaborative diagnosis and treatment Through video communication technology, doctors and patients can have video consultations. Multiple medical institutions and experts can share patients' oral health data through the cloud platform for joint diagnosis, treatment and decision-making. S6. Health management and tracking Track the patient's health data over a long period of time, analyze the patient's brushing frequency, eating habits and treatment effects, generate personalized health management plans based on the analysis results, and remind patients to conduct regular oral examinations and adjust their health through push notifications.

[0044] Embodiment 3 The third embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the oral disease treatment auxiliary management method based on cloud data of the above embodiment are implemented.

[0045] Embodiment 4 The fourth embodiment of the present invention is based on the same inventive concept. A computer device proposed by the present invention comprises: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory, and execute the oral disease treatment auxiliary management method based on cloud data of the above embodiment.

[0046] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0047] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An oral disease treatment auxiliary management system based on cloud data, characterized in that: include: Data collection and preprocessing module, used to collect patients' oral data from various sensors and medical devices, and upload the data to the cloud platform; Data storage and management module, used to store and manage all collected patient data, and provide electronic health record EHR management functions; Intelligent diagnosis and prediction module, which is used to analyze patients' oral data, automatically identify oral diseases and predict potential health risks; The treatment plan recommendation and optimization module provides patients with personalized treatment plans based on the diagnosis results and individual differences of patients, and optimizes the treatment path in real time; Telemedicine and collaborative diagnosis and treatment module, used to support remote collaboration and data sharing between doctors, patients and medical institutions; The health management and tracking module is used to monitor patients' daily behaviors and treatment effects, and provide personalized health intervention and management plans.

2. The oral disease treatment auxiliary management system based on cloud data according to claim 1, characterized in that: The data acquisition and preprocessing module includes: A data acquisition unit, used to obtain the patient's oral data from oral imaging equipment, smart sensors and wearable devices, the data including oral imaging data, clinical examination data and patient daily behavior data; The data preprocessing unit is used to perform denoising, enhancement and segmentation processing on the collected oral image data to improve the data quality and facilitate subsequent analysis.

3. The oral disease treatment auxiliary management system based on cloud data according to claim 1, characterized in that: The data storage and management module includes: A data storage unit, used to store the collected patient data in a cloud database and classify and store them according to data types, including imaging data, clinical data, and behavioral data; The data security management unit is used to encrypt patient data and is responsible for the security of patient privacy data by setting up a permission management mechanism.

4. The oral disease treatment auxiliary management system based on cloud data according to claim 1, characterized in that: The intelligent diagnosis and prediction module includes: A data analysis unit, used to perform preliminary analysis on the oral data collected from the patient, extract features and perform data cleaning; The data analysis unit cleans the collected oral images and clinical data by using NumPy and Pandas libraries, and uses OpenCV to perform image denoising and enhancement, with the standardized image size being 256x256 pixels and the pixel values ​​being normalized to the interval [0,1]; The data analysis unit also increases the diversity of data through data enhancement techniques, including rotation, flipping and shearing, to reduce the risk of overfitting; Deep learning diagnosis unit, which is used to automatically analyze the patient's oral images through convolutional neural network (CNN) to generate intelligent diagnosis results; The deep learning diagnosis unit uses the TensorFlow or PyTorch framework to build a CNN model. The typical CNN architecture is 3 convolutional layers and 2 fully connected layers. Nonlinear mapping is performed through the ReLU activation function. The training data set includes at least 10,000 oral image data. The model is optimized through transfer learning technology, so that the model accuracy reaches at least 85%; During the training process, the cross-entropy loss function Cross-EntropyLoss was used for model optimization, the Adam optimizer was used, the initial learning rate was set to 0.001, the batch size was 32, and 50 epochs were trained; The disease prediction unit combines the patient's health history, lifestyle data and known medical data to predict potential oral health risks through machine learning algorithms and provide early warnings; The disease prediction unit establishes a prediction model based on the patient's health history and lifestyle by applying a support vector machine SVM, a random forest RF or a gradient boosting tree XGBoost algorithm to calculate the risk probability of oral disease occurrence; The disease prediction unit uses a k-fold cross-validation method to optimize the model, with the goal of making the area under the AUC curve greater than 0.85; When the predicted value exceeds the set threshold, a push notification is used to remind the patient to seek medical attention and undergo an oral examination in a timely manner.

5. The oral disease treatment auxiliary management system based on cloud data according to claim 1, characterized in that: The treatment plan recommendation and optimization module The following units are included: Personalized treatment recommendation unit, which generates personalized treatment plans based on intelligent diagnosis results, patient health status and preferences; The treatment optimization unit optimizes the treatment pathway in real time and adjusts the treatment plan according to the patient's treatment progress by applying optimization algorithms, including genetic algorithms or simulated annealing.

6. The oral disease treatment auxiliary management system based on cloud data according to claim 1, characterized in that: The telemedicine and collaborative diagnosis and treatment module includes the following units: Remote video consultation unit, which supports real-time video communication between doctors and patients through WebRTC technology for remote diagnosis and treatment recommendations; The data sharing unit uses the FHIR standard protocol to enable doctors, patients and experts to share patients' medical data securely and in real time, and conduct collaborative diagnosis and treatment across regions and institutions.

7. The oral disease treatment auxiliary management system based on cloud data according to claim 1, characterized in that: The health management and tracking module includes the following units: Health monitoring unit, used to collect patients' health behavior data in real time, including tooth brushing frequency and eating habits, and monitor patients' health status; The health advice generation unit generates personalized health intervention measures based on the patient's health data and feedback, including oral health advice and dietary recommendations, and pushes health reminders to patients.

8. A method for assisting management of oral disease treatment based on cloud data, characterized in that: The oral disease treatment auxiliary management system based on cloud data as claimed in any one of claims 1 to 7 comprises the following steps: S1. Data collection and preprocessing The patient's oral data, including oral images, clinical examination data and patient behavior data, are collected through smart toothbrushes, digital imaging devices, oral endoscope medical devices and sensors. The data are transmitted to the cloud platform through wireless communication protocols and enter the pre-processing stage. The data quality is enhanced through image denoising, enhancement and standardization processing to prepare for subsequent analysis. S2. Data storage and management The collected patient data is stored in the distributed database of the cloud platform. The data is stored in categories, including imaging data, clinical data and behavioral data. Data access is controlled through role-based permission management (RBAC). S3, Intelligent Diagnosis and Prediction Use deep learning algorithms to automatically analyze patients' oral imaging data, identify potential oral diseases and generate intelligent diagnostic results. Combined with patients' health history and lifestyle data, machine learning algorithms are used to predict potential oral disease risks and provide early warnings. S4. Treatment plan recommendation and optimization Based on the intelligent diagnosis results, a personalized treatment recommendation algorithm is used to generate a treatment plan for the patient. Combined with the patient's health status and treatment feedback, an optimization algorithm is used to optimize the treatment path, and the treatment plan is adjusted and improved in real time. S5. Telemedicine and collaborative diagnosis and treatment Through video communication technology, doctors and patients can have video consultations. Multiple medical institutions and experts can share patients' oral health data through the cloud platform for joint diagnosis, treatment and decision-making. S6. Health management and tracking Track the patient's health data over a long period of time, analyze the patient's brushing frequency, eating habits and treatment effects, generate personalized health management plans based on the analysis results, and remind patients to conduct regular oral examinations and adjust their health through push notifications.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the oral disease treatment auxiliary management method based on cloud data as described in claim 8 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by the processor, the oral disease treatment auxiliary management method based on cloud data as claimed in claim 8 is implemented.

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