432 results about "Anatomical feature" patented technology

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

The invention discloses an AI-based neurosurgery auxiliary robot visual positioning system, and relates to the field of visual positioning, which comprises the steps of deploying and initializing structured light scanning equipment and near-infrared imaging equipment, carrying out multi-modal image acquisition on a surgical area, and carrying out standardization processing, space-time alignment and fusion on the acquired image. A multi-modal visual acquisition and preprocessing module, a three-dimensional tissue model construction and registration module, a visual-anatomical feature recognition and extraction module, an AI auxiliary positioning and path optimization module, an intraoperative dynamic perception and feedback control module and a target position confirmation and instruction output module are constructed. According to the method, high-precision identification and dynamic modeling of a brain tissue structure are realized, real-time identification, path planning and position correction can be carried out on a key anatomical structure in an operation process, the precision, the intelligent level and the intra-operation response capability of neurosurgery operation are remarkably improved, the operation risk is effectively reduced, and the positioning reliability and the automatic control efficiency are improved.

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The invention discloses an in-vitro bionic mechanical testing system and method for spinal skeletal muscles, and particularly relates to the field of mechanical testing, and the system comprises a bionic construction module, a finite element modeling module, a multi-axis mechanical loading module, an iterative calculation module and a correction module; through modular design, the system comprises a vertebral body unit, an intervertebral disc simulation assembly and a skeletal muscle simulation unit, each assembly is customized according to anatomical features of a human body, a finite element modeling module is utilized to generate a three-dimensional model, a multi-directional mechanical load is applied, and response data is collected; experimental data are input into the finite element model for iterative calculation, so that stress distribution, a strain field and kinematic deviation are compared; if the error exceeds a preset threshold value, the model parameters and the physical parameters are corrected until the experiment and simulation results converge; and finally, based on the optimized finite element model, predicting a failure mechanism of the spinal skeletal muscle under a complex load, and evaluating the risks of fracture, hairline fracture, nucleus pulposus protrusion and muscle strain.

The invention relates to the field of medical image segmentation, and discloses a precision surgery-oriented robot medical image segmentation and feature extraction method, which comprises the steps of constructing a dynamic segmentation network model of a bidirectional attention architecture, and inputting a preprocessing module for feature extraction and generating a multi-scale feature pyramid; the Transform coding branch is used for time sequence feature modeling and outputting a time sequence enhancement feature; the convolutional coding branch is used for enhancing anatomical features and surgical instrument features and outputting spatial enhancement features; the multi-stage feature fusion unit is used for performing multi-stage iterative fusion and outputting final fusion features; the decoding output module is used for decoding and generating pixel-level segmentation masks of the anatomical structure and the surgical instrument; training the dynamic segmentation network model; and performing medical image segmentation and feature extraction based on a medical image video sequence input in real time by using the trained dynamic segmentation network model. Accurate segmentation of anatomical tissues and dynamic instruments in an operation scene is realized.

The invention relates to the technical field of tumor interventional operations, and discloses a visual navigation method based on a tumor interventional operation robot. The method comprises the following steps: acquiring real-time medical image data of a tumor area containing multi-modal imaging information so as to comprehensively present anatomical details; and performing three-dimensional reconstruction on the image data to generate a tumor area three-dimensional anatomical structure model capable of visually displaying a space structure. Key anatomical feature points are extracted based on the model, space coordinates are calculated, a surgical robot intervention path is planned according to the coordinates, and an initial navigation track is generated; and continuously collecting real-time pose data of the robot in an operation, dynamically matching the real-time pose data with the initial navigation trajectory, adjusting motion parameters according to a matching result, and generating a corrected navigation instruction. The method can reflect the intraoperative anatomy condition in real time, dynamically optimize the path, solve the problems that traditional navigation depends on preoperative static images and lacks real-time adjustment, reduce operative complications and improve the treatment effect of patients.

The invention relates to a chest pain classification method and system based on multi-modal data fusion and a deep learning model, and belongs to the technical field of intelligent medical auxiliary diagnosis. The method comprises the following steps: firstly, carrying out heart region segmentation and pathological feature enhancement preprocessing on a chest radiograph image, extracting anatomical features by adopting a multi-scale visual model which is optimized and improved aiming at the chest radiograph, meanwhile, carrying out multi-lead time sequence calibration and ST-segment waveform marking on an electrocardiogram signal, and capturing electrophysiological time sequence features through a multi-scale one-dimensional convolutional network; then, a dynamic gating hybrid expert system is constructed, an anatomy expert, an electrophysiology expert, a multi-modal association expert and a critical value expert process specific modal features respectively, and a gating network dynamically calculates expert weights based on real-time vital signs and pathological features; according to the method, by integrating multi-modal data and combining a deep learning technology, rapid and accurate chest pain classification support and report generation can be provided for clinicians, and the method has a wide application prospect.

The invention discloses an edentulous jaw dental implant positioning method combined with depth variation reasoning. The method comprises the following steps: S1, generating optimized medical image data; s2, generating a jaw bone three-dimensional reconstruction model of the edentulous jaw patient; s3, generating anatomical feature data; s4, generating a preliminary implant positioning scheme; s5, generating a local bone reconstruction simulation result; s6, performing dynamic interaction on a local bone reconstruction simulation result and the preliminary implant positioning scheme, optimizing the preliminary implant positioning scheme through a depth variation inference model, and generating an implant optimized positioning scheme including mechanical stability and biomechanical performance evaluation; and S7, based on the implant optimization positioning scheme, generating implant guide plate design data and surgical navigation data in combination with the jaw bone three-dimensional reconstruction model of the patient. According to the method, the final implant positioning point obtains a globally optimal solution in the aspects of long-term stability and biological adaptability.

An orthopedic tool includes a main body and a reference member. The reference member may be configured to engage an anatomical feature of a patient, such as the patient's bone. The angle of the reference member may be adjusted using a knob.

The invention discloses a digital imaging method of an ear-nose-throat examination endoscope, and relates to the technical field of medical treatment, and the method comprises the following steps: synchronously collecting a white light image, a photoacoustic signal, a stimulated Raman spectrum, pressure sensor data and a physiological signal of a target orifice through a multi-physical field probe; constructing a four-dimensional tensor bound with the anatomical features; based on a vocal cord vibration fundamental frequency harmonic characteristic optimization graph convolutional network, dynamically constructing an adjacent matrix and coupling cross-modal characteristics to generate a submucosal lesion enhanced image; a deformable convolutional network constrained by vocal cord biomechanics is adopted, and the shape of a convolution kernel is dynamically adjusted according to the relation between real-time strain and elastic modulus, so that motion artifacts caused by swallowing actions are inhibited; generating a curvature-driven asymmetric convolution kernel based on ear canal spiral geometry, and executing super-resolution reconstruction in combination with confrontation training of fractal constraint; and fusing the white light image gradient and the pressure gradient field, and outputting a three-dimensional lesion contour consistent with the anatomical structure.

The invention provides an echocardiogram video abstraction method based on a hierarchical space-time attention mechanism, and the method comprises the steps: obtaining an echocardiogram, inputting the echocardiogram into an anatomy and physiological basic model of a key frame recognition task, and obtaining key frame information; inputting the key frame information into a multi-dimensional spatial-temporal feature extraction model to obtain feature information and frequency domain information; fusing the feature information and the frequency domain information; performing key frame enhancement processing on the fusion information by adopting an SE channel attention mechanism to obtain key frame anatomical feature expression; processing the key frame anatomical feature expression by adopting a key frame backtracking strategy and a classification network to obtain a key frame identification classification result; performing left ventricle segmentation on the classified image by adopting an improved U-Net left ventricle region segmentation network; estimating the area and volume of the left ventricle based on a segmentation result; fusing the estimated left ventricular area and volume by adopting a hierarchical attention mechanism and a cross-layer information fusion strategy to obtain a ventricular volume result; according to the method, through abstract extraction of the key information of the ultrasonic cardiac video, the clinical practicability of intelligent screening and auxiliary diagnosis of cardiovascular diseases based on ultrasonic is improved.

The invention discloses a medical image segmentation algorithm and system based on Mama, and a storage medium. The method comprises the following steps: processing medical image features through a multi-path convolution attention mixer MCAM; the VSS Block captures global information through a state space model to enhance the coding capability; the COC module extracts features through channel projection, vertical offset and horizontal offset; the up-sampling features and the features output by the MCAM are fused, the resolution of the image is gradually recovered through inverse operation, and the number of channels is adjusted to the proportion required by the segmentation task; inputting the feature maps after multiple complete coding and down-sampling into an up-sampling and visual state space module, and decoding the feature maps together with the corresponding feature maps of multiple coding cross offset connection operations; medical image segmentation is carried out, and key anatomical features and subtle pathological changes are accurately distinguished. By improving downsampling and U-Net jump connection based on Mama, the feature extraction capability and the capability of capturing complex semantic information are enhanced.

The invention discloses a diabetic foot early-stage detection method combining infrared and visible light imaging, and relates to the technical field of diabetic foot medical imaging diagnos.The diabetic foot early-stage detection method comprises the steps that multi-mode image collection and standardization processing are conducted, infrared images and visible light images are synchronously collected, and standardization processing such as size normalization is conducted; image registration and space alignment are carried out, marking points are set based on foot anatomical features, and feature extraction, mismatching point elimination and transformation matrix calculation are carried out; extracting and screening multi-dimensional features, extracting temperature and structural features, and screening by using a Relief-F algorithm; feature lesion recognition and classification are fused, and lesion probability is output through a double-branch convolutional neural network; carrying out detection result verification and feedback optimization, and comparing a clinical diagnosis optimization model; and generating a detection report and storing data, and generating a report containing the fused image. The early lesion detection precision is improved through multi-modal fusion, individual and environment differences are adjusted and adapted in a personalized mode, and reliable technical support is provided for clinic.

The invention discloses a fractal-driven laparoscope Toldt line positioning method and device and a medium. The method comprises the following steps: acquiring a plurality of laparoscope video frames; performing feature modeling on the plurality of laparoscope video frames according to a Toldt line segmentation model to obtain a multi-scale feature map; and performing multi-scale spatio-temporal feature fusion and aggregation processing on the multi-scale feature map, and inputting a processing result into a multi-scale decoder for prediction to obtain the position of the Toldt line in the abdominal cavity. According to the fractal-driven laparoscope Toldt line positioning method and device and the medium, the anatomical features of the Toldt line are captured through accurate feature modeling, the adaptability to the dynamic abdominal cavity environment is enhanced in combination with multi-scale spatial-temporal feature fusion, high-precision prediction is achieved through a multi-scale decoder, and therefore the position of the Toldt line in the abdominal cavity is recognized, and the positioning accuracy of the Toldt line in the abdominal cavity is improved. The problem that it is difficult to accurately recognize the position of the Toldt line in the abdominal cavity can be solved.

The invention discloses a diffuse large B-cell lymphoma segmentation system based on deep learning, and the system comprises a data collection module which is used for collecting PET and CT images of a patient; the double-branch encoder module is used for processing PET and CT images respectively; the cross-modal feature cross fusion module is used for extracting positioning clues from the CT anatomical features by taking the PET metabolic features as queries; taking CT structure characteristics as query, and screening PET high-metabolism regions; the global-attention suppression module is used for extracting global context features, performing down-sampling, remodeling input features into a low-dimensional matrix, then performing self-attention calculation, generating a sparse weight matrix for the input features based on a full connection layer, and then performing sparse attention calculation; and finally, fusing the two attention calculation results and outputting enhanced features. And the multi-scale jump connection module is used for fusing the encoder features and the decoder features and recovering spatial details.

The invention discloses a high-precision and automatic dental crown generation method. The method comprises seven steps of oral cavity three-dimensional data acquisition, full-mouth tooth semantic segmentation, upper and lower jaw point cloud precise registration, abutment and associated gingiva precise segmentation and trimming, abutment surrounding environment multi-tissue segmentation, neck-edge line secondary precise detection, and dental crown generation and multi-dimensional refinement. The method comprises the steps of oral cavity three-dimensional data acquisition, full-mouth tooth semantic segmentation, upper and lower jaw point cloud precise registration, abutment and associated gingiva precise segmentation and trimming, abutment surrounding environment multi-tissue segmentation, neck-edge line secondary precise detection and dental crown generation. Through combination of deep learning and a point cloud algorithm, high-precision segmentation of teeth and gingiva and precise reduction of an occlusion relationship are realized, an oral anatomical feature library and a clinical repair standard are fused, and through multi-dimensional fine adjustment optimization, a dental crown 3D model which is high in fitting degree, harmonious in occlusion and capable of meeting clinical requirements is generated. The method is full-process automatic, greatly improves efficiency, reduces operation threshold, reduces material waste and diagnosis and treatment cost, remarkably improves repair success rate, and is suitable for various oral repair scenes.

The invention discloses a liver cancer image lightweight segmentation method based on UNet, and relates to the technical field of liver cancer segmentation of medical images. According to the method, a lightweight dilated convolution attention module combining local separable dilated convolution and a context anchor attention module is provided, and the feature expression ability is enhanced while the calculated amount is reduced; a self-adaptive wavelet pooling down-sampling module combining wavelet transform and a self-adaptive attention mechanism can perform further feature concentration and extraction on the feature map; a lightweight feature up-sampling and enhancement fusion module composed of a local separable cavity convolution module and a multi-frequency channel attention module can effectively improve the cross-scale feature fusion capability of the model, and maintain a low parameter amount and a low calculation amount. According to the method, while the parameter quantity and the calculation cost are remarkably reduced, the anatomical features of the liver can still be accurately captured, a finer segmentation result is provided, doctors are assisted in optimizing treatment schemes such as tumor excision and living body transplantation, and the clinical decision-making efficiency is improved.

A surgical planning system is disclosed which provides computer assisted navigation for spinal surgery. The surgical planning system includes a computer platform operative to obtain images of at least two different postures of a patient's spine, and to measure displacement of anatomical features of the patient's spine between the images of the at least two different postures of the patient's spine. The computer platform is further operative to estimate stiffness of the patient's spine based on the measurements of displacement, and to generate a patient-specific kinematic model of the patient's spine based on the estimated stiffness, and provides a surgical plan based on the patient-specific kinematic model of the patient's spine.

The invention relates to the technical field of medical data mining, in particular to a postoperative pulmonary infection prediction method and system.The postoperative pulmonary infection prediction method comprises the following steps that a continuous data set is formed by collecting breathing parameters and conducting alignment completion, tidal volume and blood oxygen response delay characteristics are extracted through sliding analysis, and a functional abnormal pulmonary segment is located through local anomaly recognition; and fusing physiological and anatomical features to establish a spatial probability map, extracting a respiratory variation coefficient and a risk feature regression analysis confidence index, and outputting an infection risk early warning list with anatomical positioning. According to the method, data faults are eliminated through timestamp alignment to improve the reliability of time sequence association, the breathing compensation capability is quantified through multi-scale window analysis dynamic coupling, lesion avoidance feature masking is accurately positioned through local anomaly detection, and the result interpretability is enhanced through combination of multi-modal feature fusion and space mapping. The three-stage progressive mechanism improves the prediction precision and timeliness, and provides a decision basis with both time sensitivity and anatomical orientation for clinic.

The invention discloses an EEG-to-three-dimensional fMRI volume generation method based on multidirectional time-frequency convolution attention and VM-UNet, and the method comprises the steps: S1, carrying out the preprocessing of an inputted EEG signal, constructing an EEG input sample in a three-dimensional tensor form, and carrying out the normalization processing of EEG and fMRI data; s2, mapping an EEG input sample to a high-dimensional feature space by using an EEG spectrogram projection module to obtain an initial embedding representation; s3, processing the initial embedded representation through a multi-directional time-frequency convolution attention encoder to obtain a multi-level fusion global feature representation, and adjusting the multi-level fusion global feature representation into a feature map with a target size; s4, the adjusted feature map is input into a Vision-MambaU-Net decoder, multi-scale decoding and spatial reconstruction are carried out, and a three-dimensional fMRI volume is generated; and S5, performing end-to-end training on the model by adopting a joint loss function to realize the generation of the volume from the EEG to the three-dimensional fMRI. According to the method, the fine anatomical features of cortex wrinkles and deep grey structures are accurately reserved, and the video memory consumption and the reasoning time are greatly reduced.

The invention relates to the technical field of medical instruments, and discloses a tracheotomy anatomical structure image recognition method and system, and the method comprises the steps: obtaining real-time visual image data and spatial positioning data of an endoscope, carrying out the time synchronization, carrying out the mode recognition of the spatial positioning data, and locking the remarkable anatomical features in the real-time visual image data, and according to the characteristics and the spatial positioning data, calculating a geometric transformation relationship between an endoscope visual coordinate system and a spatial positioning coordinate system, and finally fusing the data and displaying anatomical structure information and a surgical tool position in real time. The method can effectively solve the problem that in the prior art, an endoscope is irregularly deformed in the narrow trachea with physiological bending of a patient.

System and method for lidar-based anatomical mapping is provided. In one implementation, a LIDAR imaging technology is utilized to create a surface-based anatomical map of an exposed anatomical surface of at least a portion of the spine, or other anatomical features, using anatomical and / or artificial landmarks are disclosed. The surface mapping can then be registered with previously acquired images of at least the portion of the spine, or other anatomical features. The surface mapping can then be used to identify important structures, including an entry point in tissue (including soft tissue and bone) for the placement of a medical instrument or implant, or for Information the orientation of a medical instrument or device. The information can be displayed in an augmented reality overlay on a device display (e.g., wearable eyeglasses). The LIDAR imaging technology may be incorporated into wearable eyeglasses.

Systems and methods for artificial intelligence based, such as machine learning-based, image analysis and suggestion of optimal or desired placement of surgical appliances and / or medical alignment devices, with, in certain implementations, supervised learning provided by users. A computing device may receive a target image captured via an image sensor; process the captured target image to identify anatomical features within the captured target image; calculate, via a trained neural network or artificial intelligence algorithm, a placement orientation and position of a virtual surgical appliance and / or desired item within the identified anatomical features; and render, on a display screen, the captured target image and the virtual surgical appliance and / or desired item at the calculated placement orientation and position.

Systems and methods for fully automated anatomical analysis of an anatomical structure are provided to facilitate pre-operative planning. The computerized method may include obtaining a plurality of images, e.g., MSCT images, of patient-specific cardiovascular anatomy, and analyzing the MSCT images with a trained artificial intelligence module to identify one or more anatomical landmarks and to construct a virtual three-dimensional model of the anatomical structure. For example, the trained artificial intelligence module may execute segmentation, point detection, curve detection, or plane detection deep learning modules, independently or in combination, to identify the anatomical landmarks. The method further may include deriving anatomical measurements of the one or more identified anatomical landmarks, and displaying the virtual three-dimensional model alongside the anatomical measurements of the one or more identified anatomical landmarks.

The invention relates to the technical field of deep learning, and discloses a foot model generation method and system based on deep learning, and the method comprises the steps: collecting multi-angle foot image data of a user foot; inputting the multi-angle foot image data into a high-order embedded neural network for foot feature extraction to obtain foot feature point set data; inputting the foot feature point set data into a frequency domain diffusion guide neural network to execute image enhancement reconstruction of foot missing details to obtain enhanced foot image data; a point-line fusion neural network is utilized, foot contour points and anatomical feature lines are extracted from the enhanced foot image data at the same time, three-dimensional structure reconstruction is carried out, and foot point cloud model data are obtained.According to the method, differential modeling strategies are applied to different foot areas, the precision and the calculation efficiency are balanced, and the accuracy and the calculation efficiency are improved. The whole modeling process can be efficiently operated on a common smart phone and a cloud system, and the calculation complexity is greatly reduced.

A computer-implemented method for comparing medical images may include obtaining image data produced by multiple imaging devices indicative of anatomical features associated with an arthroplasty procedure, preprocessing the image data, including projecting three dimensional image data produced by a first imaging device of the multiple imaging devices to a plane represented by two dimensional image data produced by a second imaging device of the multiple imaging devices, identifying one or more landmarks associated with the anatomical features for comparison between images in the image data, comparing the identified one or more landmarks across the images to determine whether the images represent different individuals, and producing a notification indicative of a result of the determination of whether the images represent different individuals.

The invention relates to an intelligent control method and system for an internal cooling wet preparation nickel-titanium root canal treatment file, and solves the problems that traditional control is difficult to adapt to treatment requirements of root canals in different forms, excessive or insufficient cutting is likely to occur, and complications such as root canal wall lateral penetration and instrument breakage are caused. The method comprises the following steps: iteratively optimizing a root canal preparation strategy through a multi-target reward function by taking pressure, temperature, cleaning effect and anatomical characteristics as state spaces and taking a filing tool rotating speed, a motion mode and flushing fluid flow as action spaces, and generating a parameter combination comprising the optimal rotating speed, the optimal motion mode and the optimal flushing fluid flow; the parameter combination is output to a control system to drive the nickel-titanium root canal treatment file to execute corresponding operation, a safety threshold value is dynamically corrected according to the treatment stage and anatomical characteristics, and when the parameters deviate from the threshold value, early warning is triggered and compensation is executed. The method has the advantages that multi-parameter intelligent regulation and control are achieved, and the safety and effectiveness of root canal therapy are improved.

The invention relates to the field of remote quality control of kinesio, and provides a remote kinesio treatment guidance and quality control system. The image acquisition module is used for acquiring high-definition image data of a to-be-treated area through intelligent terminal equipment and marking anatomical feature points; wherein the high-definition image data comprises a static image and a dynamic muscle movement video stream; the AI analysis engine is used for carrying out feature extraction on the high-definition image data based on a muscle dynamics model of a to-be-treated region constructed by a convolutional neural network, identifying a muscle tension abnormal region and grading a nerve injury degree, and generating an initial sticking path recommendation scheme; the remote auditing interface transmits the recommended scheme generated by the AI to the doctor end interaction interface, obtains a target treatment scheme after the doctor end performs scheme correction based on the medical history data of the patient, and confirms a final treatment instruction through a digital signature; and the block chain quality control module is used for encrypting key operation data in the treatment process and then writing the key operation data into a distributed account book to generate a treatment process traceability chain which cannot be tampered.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical landmark detection algorithm leveraging Deep Reinforcement Learning (DRL) techniques to automatically locate one or more anatomical landmarks associated with the patient specific anatomical feature within the medical images. A 3D surface mesh model may be generated representing the patient specific anatomical features including the located one or more anatomical landmarks. The located one or more anatomical landmarks may be used to guide placement of a 3D model of a medical device that may be fused with the 3D surface mesh model to generate a patient specific 3D model of the medical device.

The invention discloses a visual interface display method and system for medical communication, and belongs to the technical field of medical communication visualization. The method comprises the following steps: acquiring an operation type, calling a corresponding scene template, synchronizing an image, completing three-dimensional reconstruction and generating an interactive anatomical model; main view real-time rendering is carried out, multi-dimensional data linkage display is realized, and a remote connection signal is obtained; when the remote connection is successful, allowing an expert to label the anatomical model; when abnormity is detected, risk early warning is triggered; calculating a network quality index, performing weighted calculation, and generating a network quality score; when the network quality score does not meet the communication condition, key anatomical feature points are extracted, and feature vectors are generated; traversing a feature database of the anatomical model, and matching with the feature vectors; positioning a focus area in the main view field, and executing area replacement logic; calling a medical network topology library, and screening alternative networks; and when no single network in the topological structure meets the requirement, starting a link aggregation mechanism.