Oral and maxillofacial head and neck tumor rehabilitation nursing system based on artificial intelligence

Abstract

The invention discloses an oral and maxillofacial head and neck tumor rehabilitation nursing system based on artificial intelligence, which belongs to the technical field of medical auxiliary systems and comprises a special data acquisition and monitoring module, a deep reinforcement learning analysis module, a biological feedback analysis module, a personalized rehabilitation plan generation module and the like. The system can monitor physiological parameters and biological feedback signals of a patient in real time, generate a personalized rehabilitation plan and provide real-time monitoring and early warning functions, in addition, the system further comprises a plurality of auxiliary modules such as a local database, a patient interaction module and an intelligent early warning and emergency intervention module, the reliability and practicability of the system are further improved, and the system is suitable for popularization and application. According to the system, the defects of a traditional rehabilitation nursing method can be overcome, the rehabilitation precision and effectiveness are improved, the participation degree and confidence of a patient are enhanced, and more comprehensive and efficient rehabilitation nursing service is provided for the oral and maxillofacial head and neck tumor patient.

Description

Technical Field

[0001] The present invention relates to the technical field of medical auxiliary systems, and more specifically, to an oral and maxillofacial head and neck tumor rehabilitation nursing system based on artificial intelligence. Background Art

[0002] Oral and maxillofacial head and neck tumors are a disease that seriously affects the quality of life of patients. During the treatment process, patients often need to undergo surgery, radiotherapy, chemotherapy and other treatment methods, which often cause varying degrees of damage to the oral and maxillofacial head and neck area of ​​the patient, leading to impaired oral function, pain, dysphagia and other problems. Therefore, rehabilitation nursing plays a vital role in the treatment of patients with oral and maxillofacial head and neck tumors.

[0003] Traditional rehabilitation care for oral and maxillofacial head and neck tumors mainly relies on the experience of medical staff and the self-perception of patients. However, this approach has many shortcomings. First, the experience of medical staff may vary due to individual differences, resulting in inaccurate and individuated rehabilitation plans. Second, the self-perception of patients is often affected by subjective factors, making it difficult to accurately reflect the progress of rehabilitation and existing problems. In addition, traditional rehabilitation care methods lack real-time monitoring and early warning mechanisms, making it difficult to detect and deal with potential rehabilitation problems in a timely manner. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In response to the problems existing in the prior art, the purpose of the present invention is to provide an oral and maxillofacial head and neck tumor rehabilitation nursing system based on artificial intelligence. Through the deep reinforcement learning analysis module and the personalized rehabilitation plan generation module, the system can generate a highly personalized rehabilitation plan according to the patient's specific condition and rehabilitation needs, which not only improves the accuracy of rehabilitation, but also ensures the effectiveness and pertinence of the rehabilitation process, helping patients to restore the functions of the oral and maxillofacial head and neck area more quickly.

[0006] 2. Technical solution

[0007] To solve the above problems, the present invention adopts the following technical solutions.

[0008] The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system includes:

[0009] A dedicated data acquisition and monitoring module is used to collect the patient's oral and maxillofacial head and neck area physiological parameters, pain level, biofeedback signals and rehabilitation progress data, and to monitor the functional recovery status of the patient's oral and maxillofacial area in real time;

[0010] Deep reinforcement learning analysis module, which uses deep reinforcement learning algorithms to analyze patient data in real time, predict rehabilitation trends, and evaluate the functional recovery efficiency of the oral and maxillofacial head and neck area;

[0011] The biofeedback analysis module analyzes the patient's facial muscle electrical activity and intraoral pressure changes to evaluate the patient's autonomic nervous system regulation ability and oral function recovery status, thereby optimizing the biofeedback training of the rehabilitation plan;

[0012] The personalized rehabilitation plan generation module generates a personalized rehabilitation plan including oral function training, pain management, nutritional intake, psychological support and biofeedback training based on the results of the deep reinforcement learning analysis module and the AI-assisted diagnosis module;

[0013] The local database is used to store various data in the above modules and historical case data and to create archives and indexes for easy retrieval and reference.

[0014] As a further improvement of the present invention, the dedicated data acquisition and monitoring module includes a wearable oral rehabilitation device, which integrates high-precision sensors that can accurately measure the patient's oral function parameters such as mouth opening, chewing strength, swallowing speed, speech clarity, etc., while capturing biofeedback signals such as facial muscle electrical activity and changes in intra-oral pressure.

[0015] As a further improvement of the present invention, the deep reinforcement learning analysis module is optimized for the field of oral and maxillofacial head and neck tumor rehabilitation, including:

[0016] Use CNN to analyze the patient's oral and maxillofacial image data, such as oral photos or videos, to assess the health status and functional recovery of the oral mucosa, teeth, and gums;

[0017] Use LSTM to analyze time series data, such as changes in the patient's mouth opening, chewing function, and swallowing function over time, to predict recovery trends and potential problems;

[0018] Use GNN to analyze the patient's oral and maxillofacial region structure data, such as the three-dimensional model of the oral and maxillofacial bones and soft tissues, to evaluate the impact of surgery or radiotherapy on the oral and maxillofacial structure.

[0019] As a further improvement of the present invention, the biofeedback analysis module includes a facial muscle electrical activity analysis unit, an intraoral pressure change analysis unit and a biofeedback training optimization unit.

[0020] As a further improvement of the present invention, the personalized rehabilitation plan generation module can generate a comprehensive rehabilitation plan including oral function recovery training, pain management, personalized dietary advice and psychological intervention according to the patient's tumor type, stage, treatment method, biofeedback response, oral and maxillofacial function recovery and individual differences.

[0021] As a further improvement of the present invention, it also includes a patient interaction module, which is connected to the patient's wearable oral rehabilitation equipment and home medical equipment through the Internet of Things technology to achieve real-time data transmission and remote monitoring. At the same time, the rehabilitation plan is adjusted in real time according to the biofeedback signal, and real-time interaction is carried out with the patient through smart devices to provide rehabilitation guidance and psychological support.

[0022] As a further improvement of the present invention, it also includes an intelligent early warning and emergency intervention module, which is used to automatically trigger the early warning mechanism when abnormal physiological parameters, biofeedback signals or rehabilitation progress deviating from expectations of the patient is detected, notify medical staff and the patient's family, and provide emergency intervention suggestions, such as suggesting immediate specific biofeedback training or contacting medical staff.

[0023] As a further improvement of the present invention, it also includes a data security and privacy protection module, which includes a data encryption unit, a data tracing unit and a data cloud storage unit, and adopts advanced encryption technology, anonymization processing and blockchain technology to ensure the security and privacy of the patient's personal information, rehabilitation data and biofeedback signals, while providing a traceable data audit function to ensure the legal and compliant use of data.

[0024] As a further improvement of the present invention, it also includes a multi-platform access and cross-device synchronization module, which includes mobile devices, computers, smart TVs and wearable devices, making it convenient for patients and medical staff to view rehabilitation plans, monitoring data and biofeedback signals at any time.

[0025] As a further improvement of the present invention, it also includes a case forward matching module, which includes:

[0026] Case database construction and update unit, used to collect, store and regularly update oral and maxillofacial head and neck tumor rehabilitation case data;

[0027] An intelligent matching algorithm unit, which is used to search and match the most similar historical cases in the case database according to the specific information of the current patient;

[0028] The case positive display unit is used to display the matched historical cases of successful rehabilitation.

[0029] 3. Beneficial effects

[0030] Compared with the prior art, the advantages of the present invention are:

[0031] (1) Through the deep reinforcement learning analysis module and the personalized rehabilitation plan generation module, the present invention can generate a highly personalized rehabilitation plan based on the patient's specific condition and rehabilitation needs. This not only improves the accuracy of rehabilitation, but also ensures the effectiveness and pertinence of the rehabilitation process, helping patients to recover the functions of the oral and maxillofacial head and neck area more quickly.

[0032] (2) The dedicated data acquisition and monitoring module and the intelligent early warning and emergency intervention module in the present invention can monitor the patient's physiological parameters and biofeedback signals in real time, and immediately trigger the early warning mechanism once an abnormality is found. This real-time monitoring and early warning system helps to timely discover and deal with potential rehabilitation problems, prevent the disease from worsening, and ensure the safety of the patient.

[0033] (3) The biofeedback analysis module of the present invention can optimize the parameters and strategies of biofeedback training according to the patient's facial muscle electrical activity and oral pressure changes. This training optimization method based on real-time data can more effectively improve the patient's autonomic nervous system regulation ability and oral function recovery state, and accelerate the rehabilitation process.

[0034] (4) The case forward matching module of the present invention can search and match historical cases with similar conditions, rehabilitation needs and physiological parameters as the current patient in the case library, providing valuable reference and reference for medical staff and patients. At the same time, the interaction and feedback between patients also help to share rehabilitation experience and insights, and promote further improvement of rehabilitation effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is the module principle diagram of the present invention. DETAILED DESCRIPTION

[0036] An implementation of the present application is described in detail below with reference to the accompanying drawings.

[0037] Example:

[0038] See also Figure 1 , an artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system, including:

[0039] 1. Dedicated data acquisition and monitoring module

[0040] Functional description: This module is used to collect the patient's oral and maxillofacial head and neck area physiological parameters, pain level, biofeedback signals and rehabilitation progress data, and monitor the functional recovery status of the patient's oral and maxillofacial area in real time.

[0041] The dedicated data acquisition and monitoring module includes a wearable oral rehabilitation device that integrates high-precision sensors such as accelerometers, pressure sensors, and electromyographic sensors. When patients wear the device, they can measure oral function parameters such as mouth opening, chewing strength, swallowing speed, and speech clarity in real time, while capturing biofeedback signals such as facial muscle electrical activity and changes in oral pressure. These data are transmitted to the system via Bluetooth or Wi-Fi for analysis.

[0042] 2. Deep reinforcement learning analysis module

[0043] Functional description: This module uses deep reinforcement learning algorithms to analyze patient data in real time, predict rehabilitation trends, and evaluate the functional recovery efficiency of the oral and maxillofacial head and neck area.

[0044] The deep reinforcement learning analysis module is optimized for the field of oral, maxillofacial, and head and neck tumor rehabilitation. For example, a convolutional neural network (CNN) is used to analyze the patient's oral and maxillofacial region image data (such as intraoral photos or videos) to evaluate the health status and functional recovery of the oral mucosa, teeth, and gums. A long short-term memory network (LSTM) is used to analyze time series data (such as changes in the patient's mouth opening, chewing function, and swallowing function over time) to predict rehabilitation trends and potential problems. In addition, a graph neural network (GNN) is used to analyze the patient's oral and maxillofacial region structure data (such as three-dimensional models of oral and maxillofacial bones and soft tissues) to evaluate the impact of surgery or radiotherapy on the oral and maxillofacial structure.

[0045] 3. Biofeedback analysis module

[0046] Functional description: This module analyzes the patient's facial muscle electrical activity and intraoral pressure changes to evaluate the patient's autonomic nervous system regulation ability and oral function recovery status, thereby optimizing the biofeedback training of the rehabilitation plan.

[0047] The biofeedback analysis module includes a facial muscle electrical activity analysis unit, an intraoral pressure change analysis unit, and a biofeedback training optimization unit. These units can analyze the patient's facial muscle electrical activity and intraoral pressure changes in real time, and adjust the biofeedback training part of the rehabilitation plan based on the analysis results. For example, if the system detects that the patient's facial muscle electrical activity is weak, the training intensity of facial muscle exercises may be increased.

[0048] 1. Facial muscle electrical activity analysis algorithm

[0049] Data preprocessing:

[0050] Filtering: High-pass filtering and low-pass filtering are used to remove noise and artifacts in facial muscle electrical signals, such as power supply noise, muscle artifacts, etc. High-pass filtering is used to remove low-frequency noise, and low-pass filtering is used to remove high-frequency noise.

[0051] Normalization: Normalize the data to eliminate differences in data from different experimental conditions. For example, the data of each channel can be normalized to zero mean.

[0052] Feature extraction:

[0053] Time domain features: Extract time domain features such as mean, standard deviation, peak value, waveform complexity, etc. of facial muscle electrical signals. These features can reflect the overall fluctuation and extreme value of muscle activity.

[0054] Frequency domain features: Frequency domain features such as frequency components and power spectrum density of facial muscle electrical signals are extracted through methods such as fast Fourier transform (FFT). These features can reflect the spectral characteristics of muscle activity.

[0055] Pattern recognition and classification: Supervised learning: Classification algorithms such as support vector machine (SVM) and random forest (RF) are used to classify facial muscle electrical signals based on the extracted features and identify different muscle activity patterns.

[0056] Unsupervised learning: principal component analysis (PCA), cluster analysis and other methods are used to reduce the dimension and cluster the facial muscle electrical signals to discover potential muscle activity patterns.

[0057] Assessment of autonomic nervous system regulation: Based on the identified muscle activity patterns, combined with the patient's physiological parameters and rehabilitation progress data, the patient's autonomic nervous system regulation ability is assessed. For example, by comparing the intensity and frequency of muscle activity in different time periods, it is possible to determine whether the patient's nervous system regulation function has improved.

[0058] 2. Oral Pressure Change Analysis Algorithm

[0059] Data preprocessing: The intraoral pressure signal was also preprocessed using filtering and normalization methods to remove noise and artifacts and eliminate data differences.

[0060] Feature extraction: Extract the time domain features of the oral pressure signal, such as the mean, standard deviation, peak value, valley value, and dynamic features such as the rate and volatility of pressure change.

[0061] Swallowing function assessment: Based on the extracted features, combined with the patient's swallowing movements and rehabilitation progress data, the patient's swallowing function recovery is assessed. For example, by comparing the changes in oral pressure in different time periods, it is possible to determine whether the patient's swallowing function has improved. Machine learning algorithms, such as decision trees and neural networks, can be used to establish a swallowing function assessment model to achieve automatic assessment and prediction of the patient's swallowing function.

[0062] 3. Biofeedback Training Optimization Module

[0063] Optimization of electromyographic biofeedback training: According to the analysis results of facial muscle electrical activity, the parameters and intensity of electromyographic biofeedback training are adjusted. For example, for patients with weak muscle strength, the intensity and frequency of training can be increased; for patients with abnormal muscle activity patterns, their muscle activity can be corrected through specific training patterns.

[0064] Optimization of pressure biofeedback training: According to the analysis results of intraoral pressure changes, the parameters and strategies of pressure biofeedback training are adjusted. For example, for patients with impaired swallowing function, their swallowing function can be improved by increasing the difficulty and frequency of swallowing training; for patients with abnormal intraoral pressure, their intraoral pressure can be adjusted through specific training methods.

[0065] 4. Personalized rehabilitation plan generation module

[0066] Function description: This module generates a personalized rehabilitation plan including oral function training, pain management, nutritional intake, psychological support and biofeedback training based on the results of the deep reinforcement learning analysis module and the AI-assisted diagnosis module.

[0067] The personalized rehabilitation plan generation module can generate a comprehensive rehabilitation plan based on the patient's tumor type, stage, treatment method, biofeedback response, oral and maxillofacial function recovery and individual differences. For example, for an oral cancer patient receiving radiotherapy, the system may recommend a rehabilitation plan that includes oral function training (such as mouth opening exercises, swallowing training), pain management (such as the use of cold compresses, painkillers), nutritional intake (such as high-protein, easily digestible dietary recommendations), psychological support (such as regular psychological counseling) and biofeedback training (such as facial muscle relaxation training).

[0068] 5. Local Database

[0069] Function description: Used to store various data in the above modules and historical case data and create archives and indexes for easy retrieval and reference.

[0070] The local database stores the patient's personal information, rehabilitation data, biofeedback signals, and historical case data. These data are carefully organized and indexed for medical staff to quickly access and analyze. For example, medical staff can search for the patient's name or medical record number to find all the data of his or her rehabilitation process, including parameters such as the degree of mouth opening and chewing strength measured each time, as well as the rehabilitation experience and treatment plans of similar patients in historical cases.

[0071] 6. Patient interaction module

[0072] Functional description: Through the Internet of Things technology, it connects with the patient's wearable oral rehabilitation equipment and home medical equipment to achieve real-time data transmission and remote monitoring. At the same time, it adjusts the rehabilitation plan in real time according to the biofeedback signal, and interacts with the patient in real time through smart devices to provide rehabilitation guidance and psychological support.

[0073] The patient interaction module allows patients to interact with the system through mobile apps or smart devices. For example, patients can view their rehabilitation plans, monitoring data, and biofeedback signals on their mobile phones. The system will also adjust the rehabilitation plan in real time based on the patient's biofeedback signals, and send reminders and rehabilitation guidance to patients through mobile apps. In addition, patients can also communicate with medical staff in real time through mobile apps to obtain psychological support and advice.

[0074] 7. Intelligent early warning and emergency intervention module

[0075] Function description: When abnormal physiological parameters, biofeedback signals or rehabilitation progress deviating from expectations are detected, the system automatically triggers the early warning mechanism, notifies medical staff and the patient's family, and provides emergency intervention suggestions.

[0076] The intelligent early warning and emergency intervention module can monitor the patient's physiological parameters and biofeedback signals in real time. If the system detects that the patient's mouth opening suddenly decreases, the chewing force decreases, or the pressure in the mouth increases abnormally, it will automatically trigger the early warning mechanism and notify medical staff and the patient's family through text messages, phone calls or mobile phone applications. At the same time, the system will also provide emergency intervention suggestions, such as suggesting specific biofeedback training or contacting medical staff immediately.

[0077] 8. Data security and privacy protection module

[0078] Functional description: It includes data encryption unit, data tracing unit and data cloud storage unit, using advanced encryption technology, anonymization processing and blockchain technology to ensure the security and privacy of patients' personal information, rehabilitation data and biofeedback signals.

[0079] The data security and privacy protection module uses advanced encryption technology to protect patients' personal information and rehabilitation data. For example, all sensitive data is encrypted during transmission and storage. In addition, the system also uses anonymization to protect patients' privacy. For example, sensitive information such as the patient's name and medical record number is replaced with a unique identifier in the system. At the same time, the system also uses blockchain technology to ensure the integrity and traceability of the data. Any modification or deletion of the data will be recorded on the blockchain for subsequent auditing and tracking.

[0080] 9. Multi-platform access and cross-device synchronization module

[0081] Functional description: Including mobile devices, computers, smart TVs and wearable devices, which allow patients and medical staff to view rehabilitation plans, monitoring data and biofeedback signals at any time.

[0082] The multi-platform access and cross-device synchronization modules allow patients and medical staff to access the system through different devices. For example, patients can view their rehabilitation plans and monitoring data on their mobile phones, while medical staff can view and analyze the patient's data on their computers. In addition, the system also supports cross-device synchronization to ensure that the data viewed by patients on different devices is consistent.

[0083] 10. Case forward matching module

[0084] Functional description: It includes a case library construction and update unit, an intelligent matching algorithm unit and a case forward display unit, which are used to search and match historical cases with similar conditions, rehabilitation needs and physiological parameters as the current patient in the case library, and display the matching results to medical staff and patients.

[0085] The case forward matching module first collects, stores and regularly updates oral and maxillofacial head and neck tumor rehabilitation case data through the case library construction and update unit. Then, the intelligent matching algorithm unit searches and matches the most similar historical cases in the case library according to the specific information of the current patient (such as tumor type, stage, treatment method, etc.). Finally, the case forward display unit will display the matched historical cases of successful rehabilitation, including a detailed description of the case, key nodes in the rehabilitation process, and the changing trend of biofeedback signals. This information provides valuable data support and reference for medical staff, and also helps to enhance patients' confidence and enthusiasm for rehabilitation.

[0086] Specific module implementation:

[0087] Case database construction and update: The system will collect and store a large amount of oral and maxillofacial head and neck tumor rehabilitation case data, including patients' personal information (anonymized), condition description, treatment plan, rehabilitation progress, biofeedback signals and other key information. The case database will be updated regularly to ensure that the matched case data is timely and representative.

[0088] Intelligent matching algorithm: Using advanced machine learning and data mining technology, we develop an intelligent matching algorithm that can search and match the most similar historical cases in the case library based on the current patient's specific condition, rehabilitation needs, physiological parameters and biofeedback signals. The matching algorithm will comprehensively consider multiple dimensions, such as tumor type, stage, treatment method, rehabilitation progress speed, biofeedback response, etc., to ensure the accuracy and reliability of the matching results.

[0089] Matching result display and recommendation: The system will display the matched historical cases in an intuitive way, including detailed descriptions of the cases, key nodes in the rehabilitation process, and changing trends of biofeedback signals. Medical staff can make more accurate and personalized rehabilitation plans for patients based on the matching results and the rehabilitation experience and treatment plans of historical cases. At the same time, the system will also show patients the matched successful rehabilitation cases to enhance their confidence and enthusiasm for rehabilitation.

[0090] Interaction and feedback: Patients can view their matching cases through the system and communicate with other rehabilitation patients to share their rehabilitation experiences and insights. Medical staff can adjust the rehabilitation plan in real time based on the patient's feedback and rehabilitation progress to ensure that the patient obtains the best rehabilitation effect.

[0091] The above is only a preferred specific implementation of the present invention; however, the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and its improved conception within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system is characterized by: include: A dedicated data acquisition and monitoring module is used to collect the patient's oral and maxillofacial head and neck area physiological parameters, pain level, biofeedback signals and rehabilitation progress data, and to monitor the functional recovery status of the patient's oral and maxillofacial area in real time; Deep reinforcement learning analysis module, which uses deep reinforcement learning algorithms to analyze patient data in real time, predict rehabilitation trends, and evaluate the functional recovery efficiency of the oral and maxillofacial head and neck area; The biofeedback analysis module analyzes the patient's facial muscle electrical activity and intraoral pressure changes to evaluate the patient's autonomic nervous system regulation ability and oral function recovery status, thereby optimizing the biofeedback training of the rehabilitation plan; The personalized rehabilitation plan generation module generates a personalized rehabilitation plan including oral function training, pain management, nutritional intake, psychological support and biofeedback training based on the results of the deep reinforcement learning analysis module and the AI-assisted diagnosis module; The local database is used to store various data in the above modules and historical case data and to create archives and indexes for easy retrieval and reference.

2. The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system according to claim 1 is characterized by: The dedicated data acquisition and monitoring module includes a wearable oral rehabilitation device that integrates high-precision sensors and can accurately measure the patient's oral function parameters such as mouth opening, chewing strength, swallowing speed, speech clarity, etc., while capturing biofeedback signals such as facial muscle electrical activity and changes in intraoral pressure.

3. The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system according to claim 2 is characterized by: The deep reinforcement learning analysis module is optimized for the field of oral and maxillofacial head and neck tumor rehabilitation, including: Use CNN to analyze the patient's oral and maxillofacial image data, including oral photos or videos, to assess the health status and functional recovery of the oral mucosa, teeth, and gums; Use LSTM to analyze time series data, including changes in the patient's mouth opening, chewing function, and swallowing function over time, to predict recovery trends and potential problems; GNN is used to analyze the patient's oral and maxillofacial region structure data, including three-dimensional models of oral and maxillofacial bones and soft tissues, to evaluate the impact of surgery or radiotherapy on the oral and maxillofacial structure.

4. The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system according to claim 1 is characterized by: The biofeedback analysis module includes a facial muscle electrical activity analysis unit, an intraoral pressure change analysis unit and a biofeedback training optimization unit.

5. The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system according to claim 1 is characterized by: The personalized rehabilitation plan generation module can generate a comprehensive rehabilitation plan including oral function recovery training, pain management, personalized dietary advice and psychological intervention according to the patient's tumor type, stage, treatment method, biofeedback response, oral and maxillofacial function recovery and individual differences.

6. The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system according to claim 5 is characterized by: It also includes a patient interaction module, which connects to the patient's wearable oral rehabilitation equipment and home medical equipment through the Internet of Things technology to achieve real-time data transmission and remote monitoring. At the same time, it adjusts the rehabilitation plan in real time according to biofeedback signals, and interacts with patients in real time through smart devices to provide rehabilitation guidance and psychological support.

7. The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system according to claim 1 is characterized by: It also includes an intelligent early warning and emergency intervention module, which is used to automatically trigger the early warning mechanism, notify medical staff and patients' families, and provide emergency intervention suggestions when abnormal physiological parameters, biofeedback signals or rehabilitation progress deviates from expectations are detected in the patient.

8. The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system according to claim 1 is characterized by: It also includes a data security and privacy protection module, which includes a data encryption unit, a data tracing unit and a data cloud storage unit.

9. The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system according to claim 1 is characterized by: It also includes multi-platform access and cross-device synchronization modules, which include mobile devices, computers, smart TVs and wearable devices, making it convenient for patients and medical staff to view rehabilitation plans, monitoring data and biofeedback signals at any time.

10. The artificial intelligence-based oral and maxillofacial head and neck tumor rehabilitation nursing system according to claim 1 is characterized by: It also includes a case forward matching module, which includes: Case database construction and update unit, used to collect, store and regularly update oral and maxillofacial head and neck tumor rehabilitation case data; An intelligent matching algorithm unit, which is used to search and match the most similar historical cases in the case database according to the specific information of the current patient; The case positive display unit is used to display the matched historical cases of successful rehabilitation.

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