76 results about "Pre-operative evaluation" patented technology

The invention discloses a hospital obese patient intelligent weight loss dynamic matching intervention method and decision making system based on multi-modal data fusion, and relates to the field of medical health, and the method comprises the following steps: S1, carrying out the real-time collection and fusion of multi-modal data; s2, dynamic metabolism modeling and plateau period breakthrough; s3, generating a hierarchical intervention strategy; s4, performing psychological-physiological linkage intervention; and S5, performing real-time safety control and feedback optimization. By collecting multi-dimensional data, constructing the model, accurately predicting the metabolic rate, the metabolic plateau period and the sudden eating tendency, dynamically adjusting the exercise intensity and the diet scheme according to the conditions of different patients, triggering a low-intensity exercise mode for the obese patients with cardiac insufficiency and calibrating diet, ensuring the exercise safety, and improving the exercise efficiency. Operation suggestions are generated on the basis of the standard, precise preoperative evaluation is conducted, postoperative rehabilitation is optimized in combination with nerve stimulation data, dangerous postures are monitored, the sport injury risk is reduced, and personalized, safe and efficient intelligent weight reduction intervention for obese patients is achieved.

The invention discloses an ultrasonic anesthesia positioning method and system based on AI preoperative evaluation and a storage medium, and relates to the technical field of medical assistance. The method comprises the following steps: comprehensively collecting body health data of a patient, including a physical examination result, a laboratory examination result and a medical history, reasoning the body health data of the patient based on an operation risk knowledge graph model, and determining an operation risk level; if the operation risk level is higher than a preset risk level value, the operation risk is evaluated again in combination with past operation case information, and if the operation condition is met, the crowd is judged to be the high-risk crowd of the operation; an ultrasonic image of an in-vivo structure of an operation high-risk crowd is collected by utilizing an ultrasonic technology, the ultrasonic image is recognized by utilizing an image recognition AI algorithm, the relationship between an anatomical position and an adjacent part is determined, and then an anesthesia position is determined. The medical quality and the service level can be improved, and safer and more efficient surgical treatment experience can be brought to patients.

The invention discloses a thyroid image segmentation method and system for obtaining multi-scale feature fusion based on UNet and deformable convolution, and belongs to the technical field of medical image processing and computer vision. A model DAmUNet of a deformable convolution and multi-scale attention feature fusion module is introduced into a UNet network architecture, the deformable convolution is adopted in an encoder and a decoder to replace traditional convolution, in the deepest layer of the encoder, the multi-scale attention feature fusion module is adopted, encoding features of the encoder with different resolutions are used as input, and the multi-scale attention feature fusion module is used as input. Channel weighting is carried out on the deepest layer coding features through a double-input channel attention mechanism to generate fusion features; and taking the fusion features as decoder input, and finally obtaining a pixel-level segmentation result of the thyroid region. The method effectively improves the segmentation precision and robustness, is excellent in Dice, IoU and other indexes, and can be widely applied to the scenes of thyroid volume calculation, preoperative evaluation and the like.

The invention discloses a psychological state recognition method and system based on physiological signals, and relates to the technical field of intelligent preoperative evaluation and planning of thoracic surgery, and the method comprises the steps: S1, collecting multi-modal physiological signals, and forming an original signal set; s2, performing parallel preprocessing on the original signal set to generate a denoised time sequence signal; s3, dynamically generating a modal weight coefficient based on the quality parameters, and performing weighted fusion on the multi-modal time sequence signals by using the weight coefficient; and S4, inputting the fused feature vector into a personalized recognition model, and outputting psychological state probability distribution through an adaptive feature mapping layer in the model. And S5, according to the user feedback signal or the distribution offset of the continuous monitoring data, triggering online parameter updating of the personalized recognition model, and generating a recognition model after incremental optimization. According to the psychological state recognition method and system based on the physiological signals, the problem that psychological state recognition is inaccurate due to large individual difference, strong environmental interference and weak dynamic adaptability can be solved.

The invention relates to the field of intelligent medical treatment and artificial intelligence, and discloses an AI image recognition-based antral sinus cancer accompanied pyloric obstruction preoperative precise evaluation system, which comprises a data acquisition and preprocessing module for acquiring and preprocessing structured clinical data and multi-modal image data of a patient; the focus identification and segmentation module is used for carrying out focus identification and segmentation processing on the preprocessed image data by adopting an improved nnU-Net framework; the feature extraction and screening module is used for extracting image features from the segmentation result and screening features related to clinical indexes; the multi-modal feature fusion module is used for carrying out feature fusion on the screened image features and the preprocessed structured clinical data; the multi-task risk assessment module is used for establishing a multi-parameter risk prediction model by adopting a semi-structured distribution regression method and outputting a multi-task prediction result; and the evaluation report generation module is used for generating a structured evaluation report. And intelligent and precise preoperative evaluation and postoperative risk prediction of the antral carcinoma patient are realized.

The invention discloses a spinal post-operation erect hypotension volume management method and system and a terminal. The method comprises the steps that individual physiological features, preoperative evaluation data, intraoperative monitoring data and post-operation monitoring data of a target patient are obtained; constructing an artificial intelligence model, and training, verifying and testing the artificial intelligence model to obtain a target evaluation model; inputting the preoperative evaluation data, the intraoperative monitoring data and the postoperative monitoring data into a target evaluation model for evaluation to obtain a risk probability; according to the individual physiological features, the preoperative evaluation data, the intraoperative monitoring data and the risk probability, making a personalized fluid infusion management scheme for the target patient; according to the fluid infusion management scheme, fluid infusion is conducted on the target patient, the patient condition is monitored in real time, and early warning information is sent out to take first-aid measures. Multi-source data are fused, the occurrence risk of the erect hypotension of the patient is evaluated and managed in real time, fluid infusion management is enhanced, and fluid infusion efficiency is improved.

The invention discloses a sensitive information safety protection method and system, and relates to the technical field of intelligent preoperative evaluation and planning of thoracic surgery, and the method comprises the steps: S1, receiving an environment sensing signal which comprises an access subject attribute, a target data attribute and an operation environment parameter; s2, generating a dynamic strategy signal and a dynamic strategy signal based on the environment sensing signal; s3, sending the dynamic strategy signal to an execution engine, and triggering the engine to generate an execution signal containing a field-level mask instruction or a record-level permission instruction; s4, performing real-time processing on the target data according to the execution signal, and generating an output signal containing a part of visible fields or a permission limited operation interface; and S5, binding the environment sensing signal and the dynamic strategy signal as an auditing traceability signal and storing the auditing traceability signal. According to the sensitive information security protection method and system, the problem of excessive protection or insufficient defense of sensitive information in a dynamic use scene due to a static protection strategy can be solved.

The invention relates to a cardiovascular and cerebrovascular risk preoperative assessment method, device and equipment for a non-cardiac surgery and a storage medium. The method comprises the steps of performing feature extraction on multi-modal preoperative data of a target patient to obtain multi-modal data features; performing cross-modal interaction processing and global modeling and fusion on the multi-modal data features by a pre-operative feature fusion module based on cross-modal global cross attention to obtain cardiovascular and cerebrovascular risk features; and inputting the cardiovascular and cerebrovascular risk characteristics into a cardiovascular and cerebrovascular risk assessment module based on multi-dimensional reconstruction loss, and outputting a cardiovascular and cerebrovascular risk preoperative assessment result based on the multi-dimensional reconstruction loss by the cardiovascular and cerebrovascular risk assessment module under the guidance of the comprehensive quantitative assessment of the to-be-predicted risk. According to the method, the efficiency and the accuracy of the pre-operative assessment of the cardiovascular and cerebrovascular risks of the non-cardiac surgery are improved, the extraction and fusion process of the multi-modal data features is optimized, and the pre-operative assessment capability and the generalization capability of the model on the cardiovascular and cerebrovascular risks are improved.

The invention relates to the technical field of medical image processing and three-dimensional reconstruction, and particularly discloses a reconstruction and analogue simulation method based on a cardiac image. The method comprises the following steps: firstly, preprocessing an input CT or MRI medical image, and automatically segmenting a heart multi-tissue structure by using an nnUNetv2 model, including a plurality of anatomical regions such as atrium, ventricle, myocardial tissue and blood pool; and then performing topology repair, smoothing processing and label resampling on the segmentation result to obtain a three-dimensional label body with a continuous structure and a complete boundary. And based on the optimized tag body, constructing a heart three-dimensional surface model by adopting a Marching Cubes algorithm, and generating a multi-material volume grid suitable for finite element analysis. According to an electrode position and a radio frequency parameter set by a user, a radio frequency ablation simulation model based on a Pennes biological heat conduction equation and an Arrhenius model is established, a temperature field and a tissue thermal damage range are calculated, and a simulation result is displayed in a three-dimensional mode in an overlapping mode. According to the method, the integrated process from medical image automatic segmentation to heart three-dimensional reconstruction and thermal simulation is realized, the segmentation precision, the modeling efficiency and the simulation reliability are improved, and the method can be applied to application scenes such as surgical planning, preoperative evaluation and medical teaching.

The invention discloses an intelligent voice rehabilitation training management system. The system comprises an intra-hospital local area server, a preoperative risk assessment module, an intra-operative neural function assessment module, an acoustic assessment module and an individualized rehabilitation training scheme making module. The system is in butt joint with intraoperative nerve monitoring equipment to collect electromyographic signals in real time, and multi-modal fusion analysis is carried out in combination with preoperative / postoperative acoustic parameters and spectrograms. The system utilizes a deep learning model to predict the noise disorder risk and the recovery trajectory at different time points after the operation; three-dimensional training navigation is constructed based on postoperative time, neurological function parameter changes and vocational voice requirements, a rehabilitation path is dynamically optimized by adopting near-end strategy optimization reinforcement learning, and training intensity and content are adjusted in a closed-loop mode through auditory feedback; closed-loop management of preoperative evaluation, intraoperative monitoring, postoperative evaluation, individualized rehabilitation and follow-up visit optimization is achieved, and the accuracy and timeliness of voice function protection and rehabilitation are improved.

A method to preoperatively assess a risk of bone fracture during or after installing a prosthetic implant into a bone of a patient, the method can include accessing patient medical information. The patient medical information can include at least a medical image of the patient. The medical image can include at least the bone that will receive the prosthetic implant. The method can also include analyzing the medical image to obtain a physical characteristic of the bone. Determining a fracture risk score based on the physical characteristic of the bone. The method can also include transmitting a fracture risk summary to a medical professional. The fracture risk summary can include at least the fracture risk score.

The invention provides an artificial intelligence-based preoperative evaluation system for maxillary posterior tooth region implantation, which comprises the following steps of: constructing an oral cavity CBCT (cone beam computed tomography) image library, and further constructing a maxillary sinus basic model, an oral cavity maxillary posterior tooth region implantation decision design model and an oral cavity maxillary posterior tooth region windowing bone grafting decision design model; constructing a maxillary sinus downstream task model according to the maxillary sinus basic model; inputting the to-be-detected oral cavity CBCT image data into the maxillary sinus downstream task model, and calling the oral cavity maxillary posterior tooth region implantation decision design model to generate a first decision or calling the oral cavity maxillary posterior tooth region windowing bone grafting decision design model to generate a second decision according to the output qualitative and quantitative characteristics, so as to carry out preoperative evaluation. According to the method, the different characteristics in the oral cavity CBCT image are intelligently identified in a whole process, and the optimal oral cavity maxillary posterior tooth area implantation decision is generated to carry out oral cavity maxillary posterior tooth area implantation preoperative evaluation, so that the reliability of the oral cavity maxillary posterior tooth area implantation preoperative evaluation is improved.

The invention provides a pancreatic fistula early warning type peritoneal drainage device and a nursing method.The device comprises a drainage body, a monitoring unit, a data processing unit and an early warning unit, the drainage body is connected between an in-vivo drainage tube and an in-vitro collection bag, and the monitoring unit is arranged in a drainage channel of the drainage body; the monitoring unit comprises a flow sensor, an amylase sensing chip and a color identification sensor; the data processing unit receives data of the monitoring unit, calculates a risk grade based on a pancreatic fistula risk grading early warning algorithm and gives an alarm through the early warning unit; the nursing method comprises the steps of preoperative evaluation, device deployment, real-time monitoring and graded intervention. The pancreatic fistula early warning system solves the problem of pancreatic fistula early warning lag, reduces nursing subjectivity, reduces manual judgment errors, improves nursing efficiency and improves patient prognosis.

This invention provides an internal fixation device for cancellous bone, belonging to the field of medical device technology. It includes a fixation plate with a plurality of screw holes and positioning holes arranged in a linear array on its top outer wall; and a guide assembly connected to the fixation plate, used to guide the hollow self-drilling screws. By incorporating the guide assembly, this invention not only guides the hollow self-drilling screws, preventing them from deviating from their pre-operatively assessed position during insertion, thus affecting their fixation effect on the fracture site, but also holds the screws during the guiding process, preventing the screw head from tilting and protruding, which could cause skin irritation and affect postoperative recovery.

The application provides a clinical feature-machine learning nomogram method for predicting prostate cancer risk stratification, relates to data processing, data analysis, machine learning and nomogram, and constructs a clinical feature-machine learning nomogram which is easy to use, good in interpretability and powerful in function by combining machine learning and nomogram technology, supports visualization of model prediction results, and realizes the function of constructing a clinical feature-machine learning nomogram for predicting prostate cancer risk stratification. Considering the simplicity and interpretability of the nomogram and the high efficiency and robustness of the machine learning model, the clinical feature-machine learning nomogram provided by the application can be used as an auxiliary tool for preoperative evaluation of prostate cancer risk stratification, and provides necessary information for individual diagnosis and treatment of prostate cancer patients.

The invention provides a method for predicting intracardiac abnormal electrophysiological distribution based on an electrocardiogram. The method comprises the steps that body surface multi-lead electrocardiogram data are obtained and preprocessed, and high-quality data fragments are obtained; constructing a lead specific multi-expert network, and extracting lead specific spatio-temporal characteristics; designing a lead weight gating module, and dynamically calculating the attention weight of each lead by adopting a multi-layer perceptron to realize sample self-adaptive lead importance evaluation; performing weighted calibration on each lead feature by using the attention weight, and inputting the reweighted features into a feature fusion and semantic enhancement network to obtain a global feature vector; end-to-end training is completed, and predicted values of the intracardiac abnormal electrophysiology distribution in all the anatomical areas are output. Through lead specific modeling and cross-lead interaction fusion, intracardiac abnormal electrophysiological distribution prediction is achieved, and a noninvasive auxiliary tool is provided for preoperative evaluation and personalized treatment scheme formulation of cardiovascular diseases such as arrhythmia.

The invention belongs to the technical field of image processing, and discloses an HSIL postoperative cutting edge state prediction method, system and device and a medium, and the method comprises the steps: obtaining a data set which comprises clinical data of a plurality of patients, colposcope images and corresponding diagnosis labels; inputting the colposcope image of each patient into the risk prediction model for classification prediction to obtain an incisal edge prediction score; screening final features in the clinical data and the cutting edge prediction score based on a feature screening method; constructing a combined model, wherein the combined model is composed of a plurality of machine learning models; training the combined model based on the selected final features, performing verification evaluation, and selecting an optimal machine learning model as a cutting edge state prediction model based on an evaluation result; and executing an incisal edge state prediction task of the patient based on the incisal edge state prediction model. According to the method, the accuracy and effectiveness of preoperative evaluation of the HSIL patient by a doctor can be improved.

The utility model discloses a novel injection needle for tumor preoperative evaluation, which belongs to the field of medical instruments and comprises an operating rod, an outer tube is arranged at one end of the operating rod, an inner tube is arranged in the outer tube, and a needle head is arranged at one end, far away from the operating rod, of the inner tube; the operating rod comprises an outer rod body, one end of the outer rod body is connected with an outer pipe, an inner rod body is arranged in the outer rod body, one end of the inner rod body is connected with the inner pipe, the other end of the inner rod body is connected with an injector filled with water, and a positioning rod is arranged on the outer wall of the inner rod body. According to the novel injection needle specially used for tumor preoperative evaluation, the positioning rod drives the inner rod body to move towards the side close to the outer tube, the inner rod body pushes the inner tube to move, the inner tube pushes the needle head to stretch out of the end of the outer tube, the needle head is inserted into the lesion position, water is injected into the inner tube through the injector, and preoperative evaluation is conducted. The operation is simple; an anti-backflow rubber sleeve is arranged to prevent water from flowing back; and the wound is pressed for hemostasis through the pressing cover with the hemostatic cotton.

The invention provides a cancellous bone internal fixing device, and belongs to the technical field of medical instruments. Comprising a fixing steel plate, a plurality of screw holes and a plurality of positioning holes are formed in the outer wall of the top of the fixing steel plate, and the screw holes and the positioning holes are distributed in a linear array mode; and the guide assembly is used for guiding the hollow self-drilling screw, and the guide assembly is connected with the fixed steel plate. By arranging the guiding assembly, the hollow self-tapping screw can be guided, the situation that the fixing effect of the hollow self-tapping screw on the fracture position of a patient is affected due to the fact that the hollow self-tapping screw deviates out of the designated position of preoperative evaluation in the screwing-in process is avoided, the hollow self-tapping screw can be held in the guiding process, and the fracture position of the patient is not affected. In this way, the situation that the screw head protrudes due to inclination, skin irritation is caused, and then postoperative recovery is affected can be avoided.

A group of marker sets capable of effectively predicting acute pain of a patient after cesarean section is screened out, and the marker sets comprise whether the patient has preoperative anxiety or not, whether the patient is gestational diabetes mellitus or not, the age of the patient, the Pittsburgh sleep quality index (PSQI) of the patient, the gestational week age of the patient and the abdominal circumference of the patient. On the basis, a visual and quantifiable column diagram prediction model is constructed, the method can be used for evaluating the risk probability of acute pain occurring after a cesarean section patient is operated clinically before an operation, targeted and individualized pain management is provided for the patient subsequently, the pregnancy safety of the patient is improved, and physical and psychological pain of the patient after the operation is relieved.

The present invention relates to a comprehensive surgical clip for improving vasectomy and an operating method thereof, comprising a comprehensive surgical clip; the comprehensive surgical clip comprises two clamps for clamping and fixing the two ends of the vas deferens, the two clamps being connected to the same scissors structure and being synchronously opened and closed by the scissors structure; and further comprising: a syringe, which is obliquely and slidably arranged on the comprehensive surgical clip and is used for injecting a mixed liquid into the vas deferens and promoting the discharge of semen. The present invention has a compact and reasonable structure and is easy to operate. By adopting a special surgical clip and a specific operating technique, work efficiency is improved. At the same time, on the basis of traditional vasectomy surgery, a special flushing technology is added to improve the efficiency of residual semen discharge, greatly shortening the patient's postoperative contraception time compared with traditional surgery, and can also judge the patency of the vas deferens according to the color of the discharged semen, providing preoperative evaluation guidance for some patients who need to restore fertility and undergo vasectomy.

The invention discloses a nasal varus papilloma root positioning method and device based on a paranasal sinus CT image. The nasal varus papilloma root positioning method based on the paranasal sinus CT image comprises the following steps: acquiring image information of a patient to be predicted; obtaining a trained positioning model; and inputting image information of a patient to be predicted into the trained positioning model so as to obtain a segmentation mask. According to the positioning model provided by the invention, high-precision segmentation and positioning of the IP root region in the CT image can be realized, the capabilities of the model in the aspects of multi-scale heterostructure recognition, key region focusing and noise robustness are improved, and then reliable technical support is provided for preoperative evaluation, surgical path planning and postoperative curative effect monitoring.

The invention provides an intelligent management system for preoperative anesthesia assessment, and the system comprises an online APP patient end which is used for a patient to carry out preoperative self-assessment and information interaction on mobile terminal equipment; the online APP doctor end is used for medical staff to check the preoperative assessment information of the patient on mobile terminal equipment and give an anesthesia risk assessment result; the offline self-service machine end is used for providing self-service evaluation service and information interaction for offline patients; and the offline PC doctor terminal is used for medical staff to check the preoperative assessment information of the patient in a hospital or a workstation and give an anesthesia risk assessment result. Furthermore, the invention further discloses an intelligent analysis method for preoperative anesthesia evaluation. Through the operation platform integrating multiple terminals and multiple scenes, full-process management from patient self-evaluation, doctor evaluation, agreement signing to report uploading is achieved, a standardized, intelligent and paperless evaluation closed loop is formed, and the efficiency and accuracy of preoperative anesthesia evaluation are improved.

The present application relates to a method, apparatus, device and storage medium for preoperative assessment of cardiovascular and cerebrovascular risks in non-cardiac surgery. The method includes: extracting features from the target patient's multimodal preoperative data to obtain multimodal data features; using a preoperative feature fusion module based on cross-modal global cross-attention to perform cross-modal interactive processing and global modeling and fusion on the multimodal data features to obtain cardiovascular and cerebrovascular risk features; inputting the cardiovascular and cerebrovascular risk features into a cardiovascular and cerebrovascular risk assessment module based on multidimensional reconstruction loss, the cardiovascular and cerebrovascular risk assessment module is guided by the comprehensive quantitative assessment of the risk to be predicted, and outputs a preoperative assessment result of cardiovascular and cerebrovascular risk based on multidimensional reconstruction loss. The present application improves the efficiency and accuracy of preoperative assessment of cardiovascular and cerebrovascular risks in non-cardiac surgery, optimizes the extraction and fusion process of multimodal data features, and enhances the model's preoperative assessment capability and generalization capability for cardiovascular and cerebrovascular risks.

The invention discloses a surgical operation evaluation method and system, a storage medium and a program product, and relates to the field of medical informatization. According to the method and the device, the evaluation system screens the matched reference surgical operation record from the historical database according to the preoperative evaluation basic parameters, segments the reference surgical operation record and the surgical operation record to be evaluated respectively, extracts the operation feature points, and evaluates the operation feature points according to the extracted operation feature points. Comprising multi-dimensional information such as position coordinates, movement speed, acting force and attitude angle of the surgical instrument, and complete reference surgical operation features and to-be-evaluated surgical operation features are generated by calculating correlation features between adjacent operation feature points. The evaluation system compares and analyzes the to-be-evaluated surgical operation characteristics with the reference surgical operation characteristics to obtain a comparison score, thereby providing a quantitative basis for evaluation of the surgical operation level, avoiding limitation of subjective evaluation, being capable of comprehensively and systematically evaluating each link of the surgical operation, and being beneficial to improving the surgical quality and the skills of doctors.

The invention relates to a method for improving safety of laparoscopic cholecystectomy based on an FMEA model and a related device. The method comprises the following steps: constructing the FMEA model containing a plurality of initial elements according to a laparoscopic cholecystectomy process in a daytime ward; acquiring real-time gallbladder images of the gallbladder and surrounding tissues through a laparoscope system, and identifying a plurality of key anatomical structures of the real-time gallbladder images; the key anatomical structure comprises a gallbladder duct, a gallbladder artery, a common hepatic duct, a common bile duct, a right hepatic artery, a Calo triangular area and peripheral blood vessel distribution; inputting the key anatomical structure, the preoperative evaluation information of the patient and the surgical operation record into an FMEA model, and prompting a risk level and a high-risk anatomical region to an operator in a visual manner; the preoperative evaluation information of the patient comprises liver function, blood coagulation function, BMI and past abdominal operation history; the surgical operation record comprises the electrocoagulation use frequency, the traction force and the surgical time. According to the method, the FMEA model is combined with the laparoscopic cholecystectomy, so that the safety and efficiency of the operation are improved.

The invention relates to the technical field of methods for treating focuses in lungs, in particular to a method for injecting medicine into peripheral cavernous focuses of lungs under the guidance of a bronchoscope ultrasonic small probe. According to the technical scheme, the medicine injection method for precise treatment of the focus in the lung under guidance of the bronchoscope ultrasonic small probe comprises the steps that firstly, preoperative evaluation is conducted on the illness state of a patient; 2, an equipment preparation stage; step 3, anesthesia; 4, bronchial insertion and focus positioning; 5, guide sheath tube fixation and medicine injection preparation are carried out; sixthly, precise medicine injection is conducted; and step 7, postoperative observation and treatment. High-resolution CT is combined with computer simulation scanning to reconstruct a focus in a three-dimensional mode, the relation between the lung focus and a bronchial tree and the path from the bronchial tree to the focus are positioned, a small ultrasonic probe is used for carrying a guiding sheath to stretch into the path, and after it is confirmed through an ultrasonic image that the small ultrasonic probe reaches the focus position, the small ultrasonic probe is withdrawn, and the focus position of the lung is determined. And performing focus medicine injection treatment in the target bronchial tube by using an injector through the ultrasonic guide sheath tube.

The present disclosure proposes an artificial intelligence (AI) based System (100) for preoperative assessment and surgical outcome prediction. The AI-based system (100) comprises one or more client modules collect data from electronic medical records (EMRs), Data repositories and Databases via a data collection module (122), then standardize and pre-process it with a standardization & pre-processing module (124) for integrity and compatibility. The pre-processed data is transmitted to a server (104) through a network(102) using an API module (126) and thereafter forwarded to an input module (112). The input module (112) authenticates and validates data of at least one patient. The data is subsequently sent to a processing module (114), and a prediction module (116) for predicting surgical duration and blood loss, and the extreme Gradient Boost model for postoperative patient placement predictions.

The embodiment of the invention provides a risk prompting system and method with a preoperative evaluation function, and relates to the technical field of operation prompting systems. The risk prompting system with the preoperative evaluation function comprises a server side, and the server side comprises a data input module which is configured to input operation related information and patient information; the evaluation module is configured to receive the patient information, calculate the physical condition score of the patient according to the patient information, estimate the operation risk and the perioperative period complexity, and obtain an evaluation result; the data storage module is configured to store data and output first data according to an evaluation result of the evaluation module; the system further comprises a user terminal connected with the server side. Through the technical characteristics of a data input module, an evaluation module, a data storage module and the like, accurate evaluation of the physical condition score of a patient, the surgical risk and the perioperative period complexity is realized.

The present invention provides a vascular image processing and analysis method and system for combined magnetic resonance vascular and vascular wall imaging, the system comprising: a data acquisition module for acquiring vascular enhanced images and vascular wall images; an image registration module for registering the vascular wall image to the vascular enhanced image to obtain the registered vascular wall image; a segmentation module for obtaining the vascular lumen segmentation result and the vascular wall segmentation result; a fusion module for weighted fusion of the vascular enhanced image and the registered vascular wall image to obtain a fused image; an analysis module for calculating the intersection area of the vascular lumen segmentation result and the vascular wall segmentation result; a three-dimensional visualization and surface reconstruction module for performing three-dimensional skeleton extraction and surface reconstruction on the three-dimensional connected domain of the suspected occluded blood vessel. The present invention can simultaneously obtain information on the vascular lumen and the vascular wall, making up for the deficiencies of traditional imaging methods in evaluating the distal vascular wall conditions of occluded blood vessels, and significantly shortening the time required for preoperative evaluation through automated processing and analysis.