93 results about "Pre-operative evaluation" patented technology

The invention provides a blood vessel image processing analysis method and system combining magnetic resonance blood vessel and blood vessel wall imaging, and the system comprises a data obtaining module which obtains a blood vessel enhancement image and a blood vessel wall image; the image registration module is used for registering the blood vessel wall image to the blood vessel enhanced image to obtain a registered blood vessel wall image; the segmentation module is used for obtaining a blood vessel lumen segmentation result and a blood vessel wall segmentation result; the fusion module is used for carrying out weighted fusion on the blood vessel enhanced image and the registered blood vessel wall image to obtain a fused image; the analysis module is used for calculating an intersection region of a blood vessel lumen segmentation result and a blood vessel wall segmentation result; and the three-dimensional visualization and curved surface reconstruction module is used for performing three-dimensional skeleton extraction and curved surface reconstruction on the three-dimensional connected domain of the suspected occluded blood vessel. According to the method, the information of the blood vessel lumen and the blood vessel wall can be obtained at the same time, the defect of a traditional imaging means in evaluating the condition of the blood vessel wall at the far end of the occluded blood vessel is overcome, and the time needed by preoperative evaluation is remarkably shortened through automatic processing and analysis.

The invention relates to the technical field of biomedicine, and discloses a rectal cancer postoperative recurrence risk prediction method based on a prediction model, and the method comprises the steps: obtaining a patient tumor in the in-vivo image data of a rectal cancer patient; extracting imaging features of the tumor of the patient, and identifying recurrence risk features of the rectal cancer patient from the imaging features; analyzing clinical pathological characteristics of the rectal cancer patient according to the preoperative evaluation data and the postoperative pathological report; key recurrence influence factors of the rectal cancer patients are identified, recurrence events and follow-up visit time of the rectal cancer patients are identified, risk scoring is carried out on the rectal cancer patients, and a risk scoring result is obtained; based on the risk scoring result, the recurrence risk stratification of the rectal cancer patient is defined, postoperative recurrence risk analysis is conducted on the rectal cancer patient in combination with the recurrence risk stratification, the recurrence risk characteristics and the risk scoring result, and a risk analysis result is obtained. According to the invention, the comprehensiveness and accuracy of the rectal cancer recurrence risk analysis result can be improved.

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 discloses an anesthesia depth evaluation method and system based on physical sign data of a patient, and relates to the technical field of medical anesthesia. An anesthesia depth evaluation system based on physical sign data of a patient comprises an anesthesia pre-operation evaluation module and an anesthesia post-operation evaluation module. By combining preoperative sign data analysis, real-time anesthesia depth evaluation, anesthesia metabolism prediction and follow-up anesthesia management, by utilizing the densely connected convolutional network, the attention mechanism and dynamic time sequence analysis, the anesthesia depth can be accurately evaluated, and the anesthesia scheme can be adjusted in real time, so that the anesthesia management of a patient is optimized, and the anesthesia efficiency of the patient is improved. The safety and individuation of the anesthesia depth in the operation process are ensured, and the anesthesia effect and the safety of a patient are improved.

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 provides a respiratory training method for rapid recovery of lung functions after general thoracic surgery, and belongs to the technical field of medical rehabilitation, the respiratory training method for rapid recovery of lung functions after general thoracic surgery comprises the following steps: performing preoperative evaluation, measuring parameters such as vital capacity, making a personalized training plan in combination with age, basic diseases and operation types, and establishing a personalized training plan for rapid recovery of lung functions after general thoracic surgery; the method is divided into an early stage, a middle stage and a later stage, and abdominal respiration and lip contraction respiration training is carried out in the early stage; in the middle stage, deep respiration and excitation type spirometer training are increased; resistance breathing and breathing exercise training is conducted in the later stage, parameters such as breathing frequency and subjective feeling of patients are closely monitored in the training, the training intensity and method are adjusted according to conditions, accurate training can be conducted according to individual differences of the patients, the lung function rehabilitation efficiency is effectively improved, complications are reduced, and the life quality of the patients is improved. The device is suitable for general thoracic surgery postoperative patients.

The invention relates to a construction method of a model for preoperative evaluation of postoperative complications of hepatic resection. Performing high-order interactive modeling on the plurality of preoperative characteristic variables, and identifying the potential influence of the multi-factor joint effect on the postoperative complications; comprising the interaction between hepatic insufficiency and diabetes and the relationship between preoperative anemia and malnutrition; using a decision tree model to screen out features matched with the complication risk by calculating each variable and interaction item; for the modeling of the nonlinear relationship between the variables, capturing the nonlinear relationship between the variables by adopting a support vector machine (SVM) method; the prediction of the increase of the preoperative liver hardness is in a non-linear relationship with the risk of postoperative complications; constructing a multi-level regression model including hierarchical Bayesian regression, performing risk assessment on different levels including an individual level and a group level, and integrating individual differences and group features of patients; and each layer of model independently evaluates different risk factors, and integrates data of different layers for prediction.

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.

The invention relates to the field of medical diagnosis, in particular to a preoperative evaluation and R0 excision prediction model and prediction method for ovarian cancer initial tumor cell deduction. The preoperative evaluation and R0 resection prediction model comprises a data acquisition module, a preoperative evaluation module and an R0 resection prediction module. The preoperative evaluation module is used for assigning and calculating tumor diameter data according to tumor lesion sizes to obtain lesion scores, the R0 resection prediction module is used for calculating R0 resection prediction scores, and the R0 resection prediction scores are obtained by multiplying six factors by corresponding assignments and adding the six factors. Through individualized evaluation, the operation R0 excision rate is improved, so that the overall ovarian cancer treatment effect is improved, and the living quality and prognosis of a patient 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.

Systems, devices and methods are provided for neuromonitoring, particularly neuromonitoring to reduce the risks of contacting or damaging nerves or causing patient discomfort during and after surgical procedures, including spinal surgeries. The neuromonitoring procedures include monitoring for the presence of or damage to sensory nerves, and optionally includes additional monitoring for motor nerves. In some systems, including systems that monitor for both sensory and motor nerves, components of the monitoring systems (e.g., stimulating electrodes and response sensors), may be combined with one or more surgical instruments. The systems, devices, and methods provide for pre-surgical assessment of neural anatomy and surgical planning, intraoperative monitoring of nerve condition, and post-operative assessment of nerve position and health.

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.

Devices, systems, and methods thereof for assessing patient specific data. A system for preoperatively assessing a patient may include a wearable device, such as a vest having a plurality of segmented compartments configured to apply force(s) onto the patient's spine, a pump connected to the vest and configured to inflate the compartments of the vest, and a computer for receiving patient specific data from the vest. The force(s) and resulting displacement to the spine may be measured and used to calculate the stiffness of the spine, range of motion, flexibility, or other biomechanical information for the patient.

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.