Remote medical service and cooperation system for pediatric department and orthopedics department

Abstract

The invention discloses a pediatric orthopedic telemedicine service and cooperation system, and relates to the technical field of pediatric telemedicine, and the pediatric orthopedic telemedicine service and cooperation system comprises a child patient data collection module, an encryption transmission module, a remote diagnosis module, a treatment scheme cooperation module, a rehabilitation monitoring module, a medical record management module and a knowledge base module, multiple parties cooperate to determine a scheme, monitor rehabilitation data, store medical records by using a block chain, establish a knowledge base to help doctors to learn, and provide full-process support for child patient diagnosis and treatment. The system is advantaged in that data is accurately acquired, information security is guaranteed, a scientific scheme is formulated through multi-party cooperation, rehabilitation monitoring is carried out to timely adjust a plan, compliance is improved through combination with interesting training, block chain medical record storage, knowledge base learning assistance, intelligent early warning risk reduction and cloud computing stability guarantee are realized, and diagnosis and treatment and rehabilitation effects are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pediatric telemedicine, and particularly to a pediatric orthopedic telemedicine service and collaboration system. Background Art

[0002] The pediatric orthopedic field faces many challenges. Children's bones are in the growth and development stage, and their structures and physiological characteristics are significantly different from those of adults, which makes the diagnosis and treatment of pediatric orthopedic diseases more complex. In some remote areas or primary medical institutions, there is a lack of professional pediatric orthopedic doctors. Facing diseases such as children's fractures and congenital skeletal deformities, it is difficult to make accurate diagnoses and effective treatments. Children often need to travel long distances to large hospitals in big cities, which not only increases the economic burden on families but also may affect the rehabilitation effect of children due to delayed treatment.

[0003] Although current telemedicine technologies have developed to a certain extent, their applications in pediatric orthopedics have limitations. Traditional telemedicine systems are not precise and comprehensive enough in data collection. The diagnosis of pediatric orthopedic diseases requires detailed information on various aspects such as the appearance of the child's limbs, the sound of joint movement, and movement data. However, existing devices may not be able to clearly capture these subtle features, making it difficult for senior experts to make accurate judgments. Moreover, the security and stability during data transmission cannot be ignored. Pediatric patient information belongs to sensitive data, and once leaked, it will have a serious impact on the child and their family. However, some telemedicine systems have vulnerabilities in data encryption and transmission processes and are vulnerable to cyberattacks. At the same time, network latency may cause untimely data transmission, affecting the timeliness of diagnosis.

[0004] In the formulation of treatment plans and the rehabilitation stage, there is a lack of effective collaboration mechanisms. The communication between primary doctors and senior experts is not smooth, making it difficult to jointly develop personalized and scientific treatment plans. During the rehabilitation process, due to the lack of continuous and effective monitoring and professional guidance, the rehabilitation effect of children cannot be guaranteed. In addition, knowledge in the pediatric orthopedic field is updated rapidly, and primary doctors have limited channels to obtain the latest diagnosis and treatment knowledge and cannot apply advanced treatment concepts and methods to actual work in a timely manner, further widening the gap in the diagnosis and treatment level compared with large hospitals. Therefore, it is urgent to develop a telemedicine service and collaboration system specifically for pediatric orthopedics to solve the many problems currently faced and improve the overall diagnosis and treatment level of pediatric orthopedics. Summary of the Invention

[0005] The pediatric orthopedic telemedicine service and collaboration system proposed by the present invention aims to solve the problems mentioned in the above prior art.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A pediatric orthopedic telemedicine service and collaboration system includes the following modules: Pediatric patient data collection module: At the primary medical institutions where the pediatric patients are located, cameras and sensor devices are equipped to collect images of the patients' symptoms. An electronic stethoscope is used to collect the sounds of joint movements, and sensors are used to obtain the limb movement data of the patients. The displacement is calculated through the formula where and are the three-dimensional coordinates of the limb, and the data is transmitted to the data processing center; Data encryption and transmission module: The data is encrypted using an asymmetric encryption algorithm, and 5G communication technology and edge computing are used to optimize the transmission efficiency. The data is compressed on the edge device, and the amount of compressed data is calculated through the formula C = k×S, where S is the amount of original data and k is the compression coefficient; Remote diagnosis module: Experts use a monitor and professional diagnostic software to view the data of the pediatric patients. The software reconstructs the three-dimensional image of the fracture site, and the fracture type T is calculated through the formula The probability i of the fracture type T under the current data D where is the probability of the occurrence of this data when T i is known, P(T i ) is the prior probability of T i , and n is the total number of fracture types; Treatment plan collaboration module: A video conferencing system is established. Through screen sharing, the situation of the pediatric patients is viewed. A collaborative editing document tool is used to improve the plan. During the process of formulating the plan, the drug dosage is calculated through the formula where is the body weight, is the age, and k1, k2, and k3 are coefficients; Rehabilitation monitoring module: The primary medical institutions use wearable devices to monitor the rehabilitation data, and the range of joint movement is calculated through the formula where and are the angles before and after joint movement. The rehabilitation data is uploaded to the medical platform, and the experts adjust the rehabilitation plan; Medical record management and knowledge base module: Blockchain technology is used to store the medical records of pediatric patients, and a pediatric orthopedics knowledge base is constructed. When doctors query, the score is calculated through the formula where is the score of the query result, is the weight of the keyword , b is the total number of keywords in the knowledge base that match the query statement, and

[0007] Furthermore, it also includes an intelligent warning module. This module uses machine learning algorithms to analyze the data of the child patients and medical record data. By establishing a disease deterioration prediction model, when the data triggers the risk threshold, it sends warning messages to doctors and the families of the child patients. For child patients with slow fracture healing, the risk value is calculated through the formula where is the current fracture healing rate, is the average healing rate of the same type of fracture, and k is the risk coefficient. When exceeds the set threshold, a warning is issued.

[0008] Furthermore, it also includes a virtual reality assistance module. Using virtual reality (VR) and augmented reality (AR) technologies, it provides an immersive experience. During the rehabilitation training of child patients, with the help of AR devices, they can play rehabilitation games. In the games, corresponding rewards are given according to the accuracy of the actions completed by the child patients. The formula is where is the reward value, is the action accuracy score, is the action completion speed score, and k1 and k2 are weight coefficients.

[0009] Furthermore, the child patient data collection module is also equipped with a micro ultrasonic device, which is used to detect the internal conditions of the bones and soft tissues of child patients. Through image recognition algorithms, it analyzes ultrasonic images and identifies bone structures and lesion characteristics.

[0010] Furthermore, the data encryption and transmission module adopts quantum key distribution technology as an alternative encryption method. When the network environment is complex, it automatically switches to the quantum key encryption mode.

[0011] Furthermore, the remote diagnosis module introduces an artificial intelligence diagnosis assistant. This assistant analyzes the symptoms and examination results input by doctors, provides diagnostic suggestions and corresponding confidence levels, and uses deep learning neural network algorithms to optimize the diagnostic accuracy. The formula is where is the number of training samples, is the loss function value, is the true diagnostic result, is the predicted diagnostic result.

[0012] Furthermore, the treatment plan collaboration module has established a treatment effect evaluation sub-module. During and after the treatment process, it quantitatively analyzes the treatment effect through various evaluation indicators. The overall treatment effect score is calculated through the formula where is the functional recovery score, is the pain score, and k1 and k2 are weights.

[0013] Further, the rehabilitation monitoring module uses big data analysis technology to analyze the rehabilitation data of children, establish a rehabilitation plan recommendation model, and adopt a collaborative filtering algorithm. The formula is , where is the rehabilitation plan recommendation score for child i, is the similarity weight between child i and child j, is the similarity between child i and child j, is the effectiveness score of the rehabilitation plan adopted by child j, and m is the number of similar children.

[0014] Further, the medical record management and knowledge base module has established a doctor-patient interaction sub-module. The family members of the child can query the medical record information of the child through the mobile APP or the web page. The doctor answers the questions of the family members, and the family members feedback the actual situation during the treatment of the child to optimize the treatment plan.

[0015] Further, the entire system adopts a cloud computing architecture, distributes data storage and computing tasks on multiple cloud servers, and allocates server resources through a load balancing algorithm. The formula is , where is the server load value, is the weight of the i-th resource, is the usage of the i-th resource, d is the number of resource types. When the load of a certain server exceeds the threshold, the task will be automatically assigned to other servers.

[0016] Compared with the existing technologies, the beneficial effects of the present invention are: In terms of diagnosis, through devices such as high-definition cameras and high-precision sensors, it is possible to comprehensively and accurately collect information such as clinical symptom images of children, joint movement sounds, and limb movement data, providing detailed and accurate diagnostic basis for senior experts, and greatly improving the accuracy of diagnosis. At the same time, advanced data encryption technology is used to ensure the security of children's sensitive information during transmission, reassuring parents.

[0017] In the formulation of treatment plans, the multi-party video conferencing system and collaborative editing document tools enable real-time communication and collaboration among grass-roots doctors, senior experts, and the rehabilitation team. All parties can communicate fully, and combined with the actual situation of the child, formulate a scientific and personalized treatment plan, improving the quality and operability of the treatment plan.

[0018] During the rehabilitation stage, the wearable device continuously monitors the limb rehabilitation data of the child, and the expert adjusts the rehabilitation plan in a timely manner according to the data to ensure the effectiveness of the rehabilitation training. At the same time, the virtual reality assistance module combines rehabilitation training with interesting games, improving the enthusiasm and compliance of the child's rehabilitation training, and helping the child recover better.

[0019] Medical record management uses blockchain technology to ensure the immutability and traceability of medical record data, facilitating doctors to access the complete diagnosis and treatment information of children at any time. The knowledge base module utilizes knowledge graph technology to integrate various types of knowledge in pediatric orthopedics, making it convenient for doctors to quickly query and learn, and improving the professional level of grass-roots doctors.

[0020] The intelligent warning module can promptly detect changes in the condition of children, take countermeasures in advance, and reduce medical risks. The application of big data analysis technology in the rehabilitation monitoring module provides personalized rehabilitation plan suggestions for new children, enhancing the overall rehabilitation effect. The doctor-patient interaction sub-module facilitates communication between family members and doctors. Family members can timely understand the condition of children, and doctors can also comprehensively grasp the situation of children, further optimizing the treatment plan. The entire system is based on a cloud computing architecture, ensuring the stability and scalability of the system, and laying a solid foundation for the sustainable development of pediatric orthopedic telemedicine services. Description of the Drawings

[0021] Figure 1 It is a schematic block diagram of the pediatric orthopedic telemedicine service and collaboration system proposed by the present invention; Figure 2 It is a bar chart comparing the diagnostic accuracy rates of pediatric orthopedic patients in different regions of the pediatric orthopedic telemedicine service and collaboration system proposed by the present invention; Figure 3 It is a line chart showing the change of the treatment plan formulation time of the pediatric orthopedic telemedicine service and collaboration system proposed by the present invention with the number of collaborators; Figure 4 It is a radar chart of the satisfaction survey of children in the rehabilitation stage of the pediatric orthopedic telemedicine service and collaboration system proposed by the present invention; Figure 5 It is a pie chart showing the proportion of the satisfaction of grass-roots doctors with the system functions of the pediatric orthopedic telemedicine service and collaboration system proposed by the present invention. Detailed Embodiments

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the present invention.

[0024] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined. In addition, the terms "mounted", "connected" and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the drawings.

[0025] Refer to Figures 1 to 5 : A pediatric orthopedic telemedicine service and collaboration system, comprising the following modules: Data collection module for children: Data collection is carried out in an orderly manner in the primary medical institutions where the children are located. In order to obtain clear and accurate clinical symptom images, an industrial-grade high-definition camera is selected, which has autofocus and low-light compensation functions and can stably output 1080p resolution images under different lighting conditions. When shooting, the doctor will follow the standardized process to shoot the fracture site or limb deformity of the child from multiple angles, and take at least 3 photos at each angle to ensure that the characteristics of the disease are fully recorded. At the same time, to ensure the color reproduction and detail clarity of the image, the camera is equipped with a professional image sensor that can accurately capture subtle changes in skin texture and bone contours. The collection of joint movement sounds relies on a high-precision electronic stethoscope. The stethoscope has a built-in high-sensitivity microphone, and the frequency response range is strictly controlled at 20Hz-20kHz, which can clearly capture subtle sounds such as friction and snapping sounds during joint movement. In a quiet and undisturbed treatment room, the doctor uses a special medical coupling agent to tightly fit the stethoscope probe to a specific position around the child's joint. Each sound collection lasts for no less than 45 seconds to ensure that sufficient audio data is obtained for analysis. During the collection process, audio data is stored in real time in a lossless format and transmitted to a local data processing terminal via Bluetooth low-power technology. Limb movement data is collected with the help of advanced inertial measurement unit (IMU) sensors. These sensors are firmly fixed to key parts of the child's limbs, such as the wrist, ankle, knee and hip joints, through customized medical straps or silicone patches. When the child performs a series of preset limb movement tests, such as slow flexion and extension, rotation, translation and other movements, the IMU sensor uses the formula Calculate the limb displacement D in real time, where and The three-dimensional coordinates of the limbs at different times are obtained by the collaborative measurement of the accelerometer and gyroscope inside the IMU sensor. To ensure the accuracy and continuity of the data, the sampling frequency of the sensor is set to 120Hz, and 120 sets of motion data can be collected per second. At the same time, the sensor can also accurately measure the angle changes of limb movements, with an angle measurement accuracy of up to 0.5°. The collected motion data is transmitted to the local data processing center in an encrypted manner through the wireless communication module.

[0026] Data Encryption and Transmission Module: In the data processing center of primary medical institutions, the asymmetric encryption algorithm RSA is used to encrypt the collected data of children. First, a pair of 2048-bit RSA key pairs are generated through a professional key generation tool. The public key is used for data encryption, and the private key is properly stored by the remote medical platform of the superior medical institution in an encrypted hardware security module (HSM) to ensure the security of the private key. Before data encryption, the data is targeted compressed on the edge device according to the data type and network bandwidth conditions. For image data, the advanced JPEG-XR compression algorithm is adopted, which can effectively reduce the data volume while ensuring the image quality. The compressed data volume C is calculated by the formula C = k × S, where S is the original data volume and k is the compression coefficient. For general clinical symptom images, through a large number of experimental tests, the value of k ranges from 0.2 to 0.35. For example, a fracture site image with an original size of 5MB can be reduced to 1 - 1.75MB after compression. For audio data, the FLAC lossless compression algorithm is adopted, which can not only reduce the data transmission volume but also ensure that the sound quality is not damaged. For limb movement data, a customized compression algorithm is adopted according to the data characteristics, and the data volume is reduced by about 40% - 60% while retaining the key movement characteristics. The compressed data is transmitted to the remote medical platform of the superior medical institution through the 5G communication technology via a dedicated virtual private network (VPN) channel. The high speed and low latency characteristics of the 5G network ensure that the data can be transmitted quickly and stably. In practical applications, the average transmission delay can be controlled within 8ms, and the data transmission rate can reach more than 1Gbps, greatly improving the data transmission efficiency. At the same time, to ensure the security of data transmission, the VPN channel adopts a multi-layer encryption protocol to prevent the data from being stolen or tampered with during transmission.

[0027] Remote Diagnosis Module: Experts in superior medical institutions work in a well-equipped diagnosis room. The diagnosis room is equipped with a professional medical monitor with a 4K resolution, which can clearly display the clinical symptom images of children, ensuring that experts can observe subtle lesion characteristics. At the same time, the professional diagnosis software used by experts integrates a variety of advanced image processing and data analysis algorithms. For fracture site images, the software uses histogram equalization and adaptive histogram equalization techniques in medical image processing algorithms to improve the overall contrast and local detail clarity of the images. In addition, edge enhancement algorithms are also adopted to highlight key features such as fracture lines, enabling experts to more accurately judge the type and degree of fractures. During the diagnosis process, experts make judgments based on their rich clinical experience and the auxiliary diagnosis information provided by the system. The auxiliary diagnosis information is generated by intelligent algorithms, and the fracture type T is calculated by the formula i for the probability under the current data D where is the known fracture type Ti The probability of the occurrence of this data is obtained through deep learning and statistical analysis of a large amount of historical case data; P(T i ) is the prior probability of fracture type T i , which is determined according to epidemiological data and clinical research; n is the total number of fracture types. For example, when analyzing a set of fracture image data, the intelligent algorithm calculates the probability that the fracture belongs to a specific type as 0.85 through the analysis of features such as the shape of the fracture line and bone displacement in the image, combined with historical case data, providing strong reference for the expert's diagnosis. The expert synthesizes this information, makes an accurate diagnosis, and details the treatment suggestions, including treatment methods, drug dosage, surgical plans, etc.

[0028] Treatment plan collaboration module: To achieve efficient multi-party collaboration, a stable and reliable multi-party video conferencing system is established. Doctors in primary medical institutions, senior experts, and members of the rehabilitation team can access the conference through their respective terminal devices (such as high-performance computers, tablets). The terminal devices are equipped with high-definition cameras and noise-canceling microphones. The camera resolution is not less than 1080p, which can clearly capture the facial expressions and operation gestures of the participants; the noise-canceling microphone uses advanced digital signal processing technology to effectively filter environmental noise and ensure clear and interference-free sound transmission. Through the screen sharing function, all parties can view the child's medical records, diagnosis results, and treatment plan details in real time. At the same time, using professional collaborative editing document tools, such as a cloud-based medical collaboration document platform, all parties can modify and improve the treatment plan in the document in real time. During the process of formulating the plan, factors such as the child's age and physical condition are fully considered. For example, when calculating the drug dosage, according to the formula . Where is the child's weight, and the measurement accuracy is accurate to 0.1 kg; is the child's age, accurate to the month; k1, k2, k3 are coefficients determined according to drug characteristics and clinical experience. The coefficients of different drugs are determined by professional pharmacy guidelines and large-scale clinical research. For example, for a commonly used pediatric orthopedic anti-inflammatory drug, k1 = 0.5, k2 = 0.2, k3 = 1. All parties communicate and discuss fully through the video conference, and adjust the coefficients according to the specific situation of the child to ensure that the plan not only meets the actual situation of the child but also has good operability.

[0029] Rehabilitation monitoring module: During the child's rehabilitation process, primary medical institutions equip the child with advanced wearable devices, including smart bracelets and patch sensors. The smart bracelet uses an optical heart rate sensor and an accelerometer to real-time monitor information such as the number of steps, exercise intensity, and heart rate changes of the child's limbs. The patch sensor uses advanced bioelectrical signal detection technology to accurately monitor muscle strength changes and joint movement angles. Calculate the range of motion of the joint ROM through the formula , where and It is the angle before and after joint movement, and the measurement accuracy can reach 0.3°. For example, when a child is undergoing rehabilitation training, a patch-type sensor is used to measure the angle change of the knee joint during flexion and extension, and the range of joint movement is calculated in real time to provide accurate data for evaluating the rehabilitation effect. These devices upload the rehabilitation data to the remote medical platform regularly (such as automatically uploading at 9:00 am every day) through Bluetooth 5.0 or Low-Power Wide-Area Network (LPWAN) technology. Based on the uploaded data and combined with the child's rehabilitation progress, senior experts adjust the rehabilitation plan in a timely manner. For example, when it is found that the growth of the child's joint movement range is slow, the expert will appropriately increase the intensity of rehabilitation training, such as increasing the training time by 10 minutes or increasing the training difficulty; when the muscle strength recovers well, the training frequency is adjusted from 3 times a day to 4 times a day to ensure the effectiveness of rehabilitation training.

[0030] Medical Record Management and Knowledge Base Module: The blockchain technology is used to store the medical records of children, and a mature consortium blockchain framework, such as Hyperledger Fabric, is selected to ensure that the medical record data cannot be tampered with and can be traced. The medical record contains detailed information such as the basic information of the child, the record of the diagnosis process, the treatment plan, and the rehabilitation record. During the process of medical record entry, through a strict identity authentication and access control mechanism, it is ensured that only authorized medical staff can perform data entry and modification. A pediatric orthopedics knowledge base is constructed to collect information such as common disease cases, treatment methods, and the latest research results. Using knowledge graph technology, information such as disease names, symptom manifestations, diagnostic methods, treatment plans, and rehabilitation suggestions is associated. For example, for supracondylar fracture of the humerus in children, the knowledge graph can clearly show the common symptoms of this disease (such as elbow pain, swelling, deformity, etc.), diagnostic methods (such as X-ray examination, CT examination, etc.), treatment plans (specific methods of conservative treatment and surgical treatment), and precautions and training methods during the rehabilitation process. Using a semantic search algorithm (such as a semantic search model based on the Transformer architecture) to calculate the query score Calculate the query score , where is the weight of the keyword , b is the total number of keywords in the knowledge base that match the query statement, which is determined according to the relevance and importance of the keyword to the query content; is the similarity between the query statement and the keyword, which is obtained by calculating the cosine similarity of the semantic vectors. In this way, the query efficiency is greatly improved, which is convenient for doctors to quickly query and learn, and improves the professional level of grass-roots doctors.

[0031] In the present invention, there is also an intelligent early warning module, which uses machine learning algorithms to analyze the real-time data and medical record historical data of the child. By establishing a disease deterioration prediction model, when the data triggers a preset risk threshold, early warning information is sent to doctors and the child's family members in a timely manner. For example, for a child with slow fracture healing, the current fracture healing rate is obtained through regular X-ray image analysis. Specifically, it is the ratio of the change in the fracture line width ΔL (unit: mm) to the time interval Δt (unit: days), that is . The average healing rate of the same type of fracture is obtained based on the statistics of the same type of fracture cases, and the risk coefficient k is dynamically adjusted according to the fracture type and the child's age. Through the formula the risk value is calculated , where is the current fracture healing rate, is the average healing rate of the same type of fracture, k is the risk coefficient, and when exceeds the set threshold, an early warning is issued.

[0032] In the present invention, there is also a virtual reality assistance module, which uses virtual reality (VR) and augmented reality (AR) technologies to provide an immersive experience for doctors and children. Doctors can use VR devices to perform virtual surgical simulations on the child's fracture site, plan surgical procedures in advance, and evaluate surgical risks. During the child's rehabilitation training, AR devices are used to carry out interesting rehabilitation games to improve the enthusiasm and compliance of rehabilitation training. For example, by combining rehabilitation movements with game scenarios through AR technology, corresponding rewards are given according to the accuracy and speed of the child's completed movements. The formula is , where is the reward value, is the action accuracy score, is the score for the speed of completing the action, and k1 and k2 are weight coefficients; the action accuracy score is calculated by capturing the joint angle deviation through the AR device camera: the preset standard action angle is , the actual action angle of the child is , the deviation , and the score formula is: The speed score is calculated according to the preset action completion time and the actual completion time : .

[0033] In the present invention, the child data acquisition module is also equipped with a micro ultrasonic device for detecting the internal conditions of the child's bones and soft tissues, helping to obtain detailed disease information. This device is based on the piezoelectric crystal transducer technology and realizes signal transmission and reception through the mutual conversion between electrical signals and ultrasonic vibrations. The device frequency can be adjusted within the range of 2 - 10 MHz. For superficial detection, such as superficial soft tissues, a high-frequency setting of 8 - 10 MHz can utilize the short wavelength to achieve high-resolution imaging; for deep structures, such as bones or deep muscles, a low frequency of 2 - 4 MHz can enhance the penetrability; 6 - 8 MHz is suitable for detections at medium depths with requirements for resolution, such as the examination of tissues around joints. After obtaining the ultrasonic images, they are first preprocessed, such as noise reduction and contrast enhancement, and then the edge detection algorithm is used to outline the bone contours. The identification of lesion characteristics adopts a convolutional neural network (CNN) model trained with a large number of samples, which can quickly identify the types, locations, and scopes of lesions, providing strong support for doctors' diagnosis.

[0034] In the present invention, the data encryption and transmission module adopts quantum key distribution technology as an alternative encryption method to further enhance the security of data transmission. Quantum key distribution strictly follows the basic principles of quantum mechanics, especially the no-cloning theorem of quantum states and the uncertainty principle. In the key generation process, by emitting photons with specific quantum states, both communication parties encode information using the characteristics of the polarization states of photons. Since any measurement of the quantum state of photons will inevitably interfere with their states, when there is an eavesdropper, both communication parties can immediately detect it. When the system monitors that the network environment shows high complexity, such as in a multi-node complex network topology or facing potential network attack threats, or in scenarios where the requirements for data security are almost stringent, the data encryption and transmission module will automatically and seamlessly switch to the quantum key encryption mode, fundamentally eliminating the risk of data being eavesdropped and tampered with during the transmission process, and building a solid defense line for data security.

[0035] In the present invention, the remote diagnosis module introduces an artificial intelligence diagnosis assistant, which is trained with a large amount of case data and can analyze the symptoms and examination results input by doctors, providing multiple possible diagnosis suggestions and corresponding confidence levels. Using the deep learning neural network algorithm, the diagnostic accuracy is continuously optimized. The formula is , where is the number of training samples, is the value of the loss function, is the true diagnosis result, is the diagnosis result predicted by the model, and the model parameters are adjusted by minimizing the loss function.

[0036] In the present invention, the treatment plan collaboration module has established a treatment effect evaluation sub-module. During and after the treatment process, the treatment effect is quantitatively analyzed through a variety of evaluation indicators. For example, methods such as functional recovery scores and pain visual analogue scores are used. Through the formula Calculate the overall treatment effect score , where is the functional recovery score, is the pain score, , , etc. are the weights of each scoring index, which are dynamically adjusted according to the disease type and treatment stage. The functional recovery score adopts the Fugl-Meyer motor function scale (with a full score of 100 points). The evaluation content includes indicators such as range of joint motion, muscle strength, balance ability, and coordination ability. The total score is obtained by weighted averaging of individual scores. The pain score adopts the visual analogue scale (VAS, 0-10 points). The child self-evaluates the pain degree by sliding the scale. 0 points means no pain, and 10 points means severe pain. The weight coefficients , are dynamically adjusted according to the treatment stage. According to the evaluation results, the treatment plan is adjusted in a timely manner to improve the treatment effect.

[0037] In the present invention, the rehabilitation monitoring module uses big data analysis technology to statistically analyze the rehabilitation data of a large number of children. By comparing the rehabilitation effects under different rehabilitation plans, a rehabilitation plan recommendation model is established to provide personalized rehabilitation plan suggestions for new children. The collaborative filtering algorithm is adopted, and the formula is , where is the rehabilitation plan recommendation score for child i, is the similarity weight between child i and child j, is the similarity between child i and child j, is the effect score of the rehabilitation plan adopted by child j, and m is the number of similar children; the similarity between child i and j adopts the cosine similarity algorithm and is calculated based on the feature vectors of indicators such as age, weight, fracture type, and disease course: , represents the feature vector of child i, which includes indicators such as age, weight, fracture type, and disease course for evaluating similarity; represents the feature vector of child j, which also includes indicators in the same dimensions as child i, such as age, weight, fracture type, and disease course. Among them, the feature vectors are standardized. The rehabilitation plan effect score is evaluated by the doctor according to the following dimensions: the recovery rate of joint range of motion (accounting for 40%): normalized after calculation by ; the visual analogue pain score (VAS, accounting for 30%): conversion on a 0-10 point scale; the muscle strength recovery index (accounting for 30%): the score after standardization of professional test data. The comprehensive score is calculated by weighting each dimension. For example, if the scores corresponding to the joint range of motion, pain score, and muscle strength of a certain child after rehabilitation are 8.5, 8, and 7 respectively, then =0.4×8.5 + 0.3×8 + 0.3×7。

[0038] In the present invention, a doctor-patient interaction sub-module is established in the medical record management and knowledge base module, and the family members of children can conveniently access this sub-module through the mobile APP or the web terminal. The mobile APP uses responsive design to adapt to the screen sizes of various mainstream mobile devices, and uses the secure and stable HTTPS protocol to connect to the server to ensure the security of data transmission. The web terminal is based on an advanced Web development framework, is compatible with multiple browsers, and provides a smooth user experience. After the family members log in, they can accurately retrieve the medical record information of the children in the dedicated medical record query interface according to dimensions such as time and medical record type. These information are encrypted and stored in the cloud database to ensure privacy security. The online communication function with doctors integrates instant messaging technology. After receiving the message reminder, the doctor can quickly reply to the questions of the family members in the background management interface. The rehabilitation guidance and precautions provided by the doctor are presented in various forms such as pictures, texts, and videos, which is convenient for the family members to understand. At the same time, the family members can feedback the actual situation during the treatment process of the children, such as food preferences, sleep duration and quality, etc., which can be detailedly entered through a structured form. These data are synchronously transmitted to the doctor's end in real time to assist the doctor in comprehensively understanding the children's conditions from multiple dimensions, and then scientifically optimizing the treatment plan to improve the treatment effect.

[0039] In the present invention, the entire system adopts a cloud computing architecture, distributes tasks such as data storage and calculation on multiple cloud servers, and improves the stability and scalability of the system. Through the load balancing algorithm, the server resources are reasonably allocated, and the formula is , where is the server load value, is the weight of the i-th type of resource, is the usage of the i-th type of resource, d is the number of resource types. When the load of a certain server exceeds the set threshold, the task is automatically assigned to other idle servers to ensure the efficient operation of the system.

[0040] Refer to Figure 2 , which is a bar chart comparing the diagnostic accuracy rates of pediatric orthopedic patients in different regions; the data shows that the present system improves the accuracy rate in remote areas through the high-definition data acquisition and remote diagnosis module; this figure intuitively proves that the system breaks through geographical restrictions through telemedicine technology, solves the problem of insufficient grass-roots diagnostic capabilities, and provides data support for the accurate diagnosis of pediatric orthopedic diseases.

[0041] Refer to Figure 3 , which is a line chart showing the change of the treatment plan formulation time with the number of collaborators; with the help of the multi-party video conferencing and collaborative editing tools of the present system, only about 15 hours are required for 11 people to collaborate, and the time is shortened. The data in the figure shows that the system optimizes the process through the real-time collaboration mechanism, verifies the efficiency of remote collaborative formulation of plans by multiple experts, and meets the clinical rapid response requirements.

[0042] Reference Figure 4 , which is a radar chart of the satisfaction of children in the rehabilitation stage; it is compared from 5 dimensions such as the fun of rehabilitation training and the timeliness of guidance. Through modules such as AR rehabilitation games, the satisfaction of each dimension of this system is improved compared with the traditional mode, and the equipment has good comfort; this chart proves that the system combines fun training with medical monitoring, effectively improves the compliance of children, provides guarantee for the rehabilitation effect, and meets the requirements of humanized design of pediatric medical care.

[0043] Reference Figure 5 , which is a pie chart of the satisfaction of grass-roots doctors' functions; the proportion of the accuracy of remote diagnosis ranks first, the treatment collaboration is convenient, and the data collection is convenient; the data reflects that the system effectively improves the grass-roots diagnosis and treatment ability through modules such as blockchain medical record management and intelligent early warning; the distribution in the figure reflects the actual effect of the system in narrowing the urban-rural medical gap and promoting the sinking of resources, providing a reference basis for the digital upgrade of grass-roots medical care.

[0044] The above is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A pediatric orthopedic telemedicine service and collaboration system, characterized in that, It includes the following modules: Pediatric data acquisition module: At the primary medical institutions where the children are located, cameras and sensor devices are equipped to collect images of the children's symptoms, use an electronic stethoscope to collect joint movement sounds, use sensors to obtain the children's limb movement data, and calculate the displacement through the formula where and are the three-dimensional coordinates of the limb, and the data is transmitted to the data processing center; Data Encryption and Transmission Module: Encrypts data using asymmetric encryption algorithms, optimizes transmission efficiency using 5G communication technology and edge computing, compresses data on edge devices, and calculates the amount of compressed data through the formula C = k × S, where S is the original data volume and k is the compression coefficient; Remote diagnosis module: With the help of a monitor and professional diagnostic software, experts can view the data of children. The software reconstructs the three-dimensional model of the fracture site and calculates the probability of fracture type T under the current data D i by the formula , where is the probability of the occurrence of such data when T is known i , P(T i ) is the prior probability of T i , and n is the total number of fracture types. Treatment plan collaboration module: Establish a video conferencing system. Through screen sharing, view the condition of the child. Use the collaborative editing document tool to improve the plan. During the plan formulation process, calculate the drug dosage through the formula where is the body weight, is the age, and k1, k2, and k3 are coefficients; Rehabilitation monitoring module: Primary medical institutions use wearable devices to monitor rehabilitation data, and calculate the range of joint motion through the formula where and are the angles before and after joint motion. The rehabilitation data is uploaded to the medical platform, and experts adjust the rehabilitation plan. Medical Record Management and Knowledge Base Module: The medical records of children are stored using blockchain technology, and a pediatric orthopedics knowledge base is constructed. When doctors query, they calculate the score through the formula where is the score of the query result, is the weight of the keyword , b is the total number of keywords matching the query statement in the knowledge base, is the similarity between the query statement and the keyword.

2. The pediatric orthopedic telemedicine service and collaboration system according to claim 1, wherein It also includes an intelligent early warning module, which uses machine learning algorithms to analyze the data of children and medical records. By establishing a disease deterioration prediction model, when the data triggers a risk threshold, it sends early warning information to doctors and the families of children. For children with slow fracture healing, the risk value is calculated through the formula where is the current fracture healing rate, is the average healing rate of the same type of fracture, and k is the risk coefficient. When exceeds the set threshold, an early warning is issued.

3. The pediatric orthopedic telemedicine service and collaboration system according to claim 1, wherein It also includes a virtual reality assistance module, which uses virtual reality (VR) and augmented reality (AR) technologies to provide an immersive experience. During the rehabilitation training of children, they can play rehabilitation games with the help of AR devices, and corresponding rewards will be given according to the accuracy of the actions completed by the children in the games. The formula is , where is the reward value,[[]] is the action accuracy score,[[]] is the action completion speed score, and k1 and k2 are weight coefficients.

4. The pediatric orthopedic telemedicine service and collaboration system according to claim 1, characterized in that, The Pediatric Patient Data Acquisition Module is also equipped with a micro-ultrasound device for detecting the internal conditions of the bones and soft tissues of pediatric patients, and analyzes ultrasound images through image recognition algorithms to identify bone structures and lesion characteristics.

5. The pediatric orthopedic telemedicine service and collaboration system according to claim 1, wherein The Data Encryption and Transmission Module uses quantum key distribution technology as an alternative encryption method and automatically switches to the quantum key encryption mode when the network environment is complex.

6. The pediatric orthopedic telemedicine service and collaboration system according to claim 1, characterized in that The remote diagnosis module incorporates an artificial intelligence diagnostic assistant that analyzes the symptoms and examination results input by doctors, provides diagnostic suggestions and corresponding confidence levels, and uses deep learning neural network algorithms to optimize diagnostic accuracy. The formula is , where is the number of training samples, is the loss function value, is the true diagnosis result, is the predicted diagnosis result.

7. The pediatric orthopedic telemedicine service and collaboration system according to claim 1, characterized in that The treatment plan collaboration module establishes a treatment effect evaluation sub-module. During and after the treatment process, quantitative analysis of the treatment effect is carried out through various evaluation indicators, and through the formula calculate the overall treatment effect score, where is the functional recovery score, is the pain score, and k1 and k2 are weights.

8. The pediatric orthopedic telemedicine service and collaboration system according to claim 1, characterized in that, The rehabilitation monitoring module uses big data analysis technology to analyze the rehabilitation data of children and establish a rehabilitation plan recommendation model. The collaborative filtering algorithm is adopted, and the formula is , where is the recommended score of the rehabilitation plan for child i, is the similarity weight between child i and child j, is the similarity between child i and child j, is the effectiveness score of the rehabilitation plan adopted by child j, and m is the number of similar children.

9. The pediatric orthopedic telemedicine service and collaboration system according to claim 1, wherein The Medical Record Management and Knowledge Base Module has established a doctor-patient interaction sub-module. The families of pediatric patients can query the medical record information of the patients through the mobile APP or the web page. Doctors answer the questions of the families, and the families feedback the actual situation during the treatment of the patients to optimize the treatment plan.

10. The pediatric orthopedic telemedicine service and collaboration system according to claim 1, characterized in that, The entire system adopts a cloud computing architecture, distributes data storage and computing tasks across multiple cloud servers, and allocates server resources through a load balancing algorithm. The formula is , where is the server load value, is the weight of the i-th type of resource, is the usage of the i-th type of resource, d is the number of resource types. When the load of a certain server exceeds the threshold, tasks are automatically assigned to other servers.

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