41 results about "Pre-operative evaluation" patented technology

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 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 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 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 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 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 invention relates to the technical field of medical image analysis, in particular to a preoperative evaluation-oriented pancreatic mesangial region perivascular inflammation imageomics identification method and system. The method comprises the following steps: acquiring a multi-stage enhanced abdomen image, and generating a standardized image structure through data quality control, de-noising registration, intensity normalization and voxel resampling processing; carrying out pancreatic mesangial region blood vessel candidate region positioning, blood vessel skeleton extraction and center line tracking, and constructing an annular region-of-interest structure; performing blood vessel and surrounding tissue segmentation and mask generation, extracting traditional image omics features and coupling features reflecting an interaction relationship between the blood vessel and the surrounding tissue, and generating an effective feature set; and through domain correction, identification model training and parameter solidification, generating a reasoning report containing an inflammation identification tag and interpretability mapping. According to the invention, automatic, accurate and quantitative identification of the perivascular inflammation is realized, and objectivity and accuracy of preoperative evaluation are effectively improved.

The present application relates to the field of intelligent medical treatment and artificial intelligence, and discloses a preoperative precise evaluation system for gastric antrum cancer with pyloric obstruction based on AI image recognition, which comprises: a data acquisition and preprocessing module, which acquires structured clinical data and multi-modal image data of a patient and performs preprocessing; a lesion identification and segmentation module, which performs lesion identification and segmentation processing on the preprocessed image data by using an improved nnU-Net framework; a feature extraction and screening module, which extracts image features from the segmentation result and screens out features related to clinical indicators; a multi-modal feature fusion module, which performs feature fusion on the screened image features and the preprocessed structured clinical data; a multi-task risk evaluation module, which establishes a multi-parameter risk prediction model by using a semi-structured distribution regression method and outputs multi-task prediction results; and an evaluation report generation module, which generates a structured evaluation report. The present application realizes intelligent and precise preoperative evaluation and postoperative risk prediction for gastric antrum cancer patients.

The invention provides a perioperative period management method based on a doctor side and related equipment, and is applied to the technical field of data processing. The method comprises the following steps: receiving target user evaluation information, a preset eye early warning model and a training sample set sent by a doctor terminal; processing the training sample set based on a preset processing rule to generate a target training set and a target verification set; processing a preset eye early warning model based on the target training set and the target verification set to generate a target eye early warning model; processing the target user evaluation information based on the target eye early warning model to generate eye high-risk label information of the target user; processing the eye high-risk label information of the target user to generate preoperative evaluation information; the target user is processed based on the preoperative evaluation information, and intraoperative early warning information is generated; and processing the preoperative evaluation information and the intraoperative early warning information to generate postoperative early warning information.

The application discloses a condylar resorption preoperative prediction method and system based on imageomics feature absolute difference screening, and belongs to the field of medical image processing. The method comprises the following steps: collecting preoperative and postoperative condylar CBCT images of a patient, respectively extracting an imageomics feature set and calculating the absolute difference of the corresponding dimension features to generate a feature difference vector; calculating the correlation of the vector and a clinical outcome, and screening out feature dimensions sensitive to bone resorption changes; locking the corresponding core feature subset in the preoperative image through reverse mapping; training a machine learning prediction model using the subset, and constructing a prediction model that only depends on the preoperative image. Compared with a traditional preoperative feature model, the application can effectively screen out core features with "evolution directionality", which helps to improve the prediction performance, feature compression effect and robustness of the model, and provides technical support for preoperative evaluation of condylar resorption risk after orthognathic surgery.

This invention comprises base plate and glenosphere designs, as well as cutting guides and holder instruments, developed to provide stronger fixation with a more minimally invasive approach to the shoulder in patients undergoing reverse shoulder arthroplasty for any reason. This newly developed technique allows for surgical operation in a more confined space, particularly in challenging cases such as tight shoulders. In addition, it offers advantages such as being usable as an alternative technique in osteoporotic patients with high cortical fixation and in revision surgeries or in cases with reduced bone stock due to various reasons. In standard base plate designs, it is known that fixation is predominantly achieved in the trabecular bone by screws inserted from the reamed peripheral bone surface surrounding the implant. However, in osteoporotic patients, trabecular bone becomes weakened and the bone stock may not provide sufficient support. Furthermore, version differences related to anatomical variations of the glenoid seen among patients may cause problems in component placement and screw insertion if proper preoperative assessment is not performed. Moreover, in classical component design, in order to send the guidewire in the correct direction corresponding to the surface where the central peg will be applied, soft tissue dissection exposing the entire glenoid must be performed. Performing this dissection may pose challenges for the surgeon in tight shoulders, increase the duration of surgery, and carry risks of complications such as instability or injury to neurovascular structures. In addition, in classical component design, the acceptable threshold of contact between the glenoid and base plate is considered to be 80%. If this is not achieved, implant failure may occur after prosthesis application. When the contact surface between the glenoid and base plate falls below 50%, graft application becomes indicated. Although it is a successful method, it is technically demanding, and in cases of graft non-union, alternative options are limited. Another method is the use of augment components for the defective area. This application not only presents high technical difficulty but also requires well-prepared three-dimensional preoperative planning. Custom-made implants are another option and are technically the most laborious and expensive to manufacture. With this invention, compared to previous techniques, the management of glenoid defect and excessive version problems can be achieved by providing higher cortical fixation with anterior-weighted support.

The invention relates to the technical field of medical treatment, and discloses a lumbar fracture bone cement distribution prediction method based on artificial intelligence, and the method comprises the following steps: S1, data acquisition and screening; s2, image preprocessing and format standardization; s3, sketching a region of interest; s4, image omics feature extraction; s5, constructing and training a prediction model; s6, model verification and prediction; and S7, clinical decision assistance. According to the artificial intelligence-based lumbar fracture bone cement distribution prediction method, the high-dimensional image omics characteristics are extracted from the three-dimensional CT image, and the machine learning model is combined, so that the bone cement distribution can be accurately predicted before an operation, the empirical operation depending on fluoroscopy in the operation is reduced, and the risk of complications such as bone cement leakage is remarkably reduced; meanwhile, clinical decision making and operation scheme optimization are assisted, an objective prediction tool is provided for a doctor, the doctor is helped to evaluate the operation scheme and select the feasibility of unilateral vertebroplasty before an operation, and high-risk patients are recognized in advance, so that a treatment strategy is optimized.

The present application relates to the medical image intelligent recognition, three-dimensional reconstruction and medical rapid prototyping technical field, solve the defect that ultrasound, nuclear magnetic imaging is not intuitive to deep lesion, there is no 1:1 entity reference before operation, the communication difficulty of doctor and patient, the system contains the shell, high-definition display module, self-locking angle support and four core components: (1) ultrasonic detection assembly (ultrasonic probe acquisition assembly), (2) image processing mainboard (AI image main control processing machine case), (3) high-precision servo drive module, (4) 3D precision printing forming bin.Probe acquisition pelvic ultrasound data, complete lesion segmentation and three-dimensional reconstruction by AI, and print entity model.The present application adopts self-developed AI, clearly shows the relationship between lesion and pelvic floor, rectum, ovarian infiltration, adapts to gynecological difficult lesions, and assists clinical preoperative evaluation.

The invention discloses a traditional Chinese medicine orthopedics postoperative acupoint physiotherapy and functional rehabilitation cooperation method, and belongs to the technical field of traditional Chinese medicine rehabilitation. According to the method, a'pathogenesis-acupoint-training-time sequence 'four-dimensional collaborative model is constructed, and the method comprises the steps of preoperative evaluation and intervention; according to four stages of an ecchymoma stage, a recovery stage, a rehabilitation later stage and a rehabilitation consolidation stage, pre-training-in-training-post-training time sequence linkage acupoint physiotherapy and functional rehabilitation are implemented, and dynamic cooperation of time, space, strength and individuals is achieved; the scheme is adjusted in a closed-loop mode through a basic-version or advanced-version biological feedback mechanism; and establishing an evaluation system and home collaborative guidance. The traditional Chinese medicine meridian theory and the modern rehabilitation technology are fused, the problems that an existing rehabilitation scheme is poor in collaboration, insufficient in individualization and low in efficiency are solved, the swelling and pain relieving and joint function recovering time can be remarkably shortened, complications are reduced, the rehabilitation efficiency and compliance are improved, and the method is suitable for medical institutions at all levels and home rehabilitation scenes.

The application discloses a liver intersegmental vein three-dimensional image artificial intelligence identification method and related equipment, and belongs to the medical image processing field. The method comprises the following steps: acquiring abdominal medical image data of a target patient; establishing an original liver segment model and an extended liver segment model of the target patient based on the image data, and extracting an intersegmental liver vein image in an intersection region of the original liver segment model and the extended liver segment model; the original liver segment model is obtained by reconstruction based on the abdominal medical image data of the target patient, and the extended liver segment model is obtained by extension operation based on the original liver segment model; inputting the extracted intersegmental liver vein image into a pre-trained binary classification model to obtain a liver intersegmental vein three-dimensional image artificial intelligence identification result. The application converts the recognition and identification of the intersegmental liver vein into an automatic extraction and intelligent identification process, and provides accurate and repeatable preoperative evaluation basis for liver surgery planning.

The invention provides a PFO plugging preoperative and postoperative evaluation method based on multi-modal ultrasound integration radiography, and the method comprises the following steps: S1, preoperative evaluation and surgical planning: collecting preoperative multi-modal ultrasound data, extracting PFO dissection and hemodynamic quantitative parameters, and outputting plugging device model and release position suggestions. The method comprises the following steps: constructing a pre-operation and post-operation quantitative evaluation system, collecting data such as 3DTEE and cTEE 24-48 hours before an operation, and extracting PFO dissection and blood flow parameters; data are collected within 10-15 minutes after an operation, parameters such as the compression ratio of the plugging device are provided, composite scores are calculated through a weighting formula after preprocessing, subjective traditional evaluation is avoided, the plugging effect can be accurately reflected, and an objective basis is provided for curative effect judgment.

The application discloses a method and device for positioning a base of inverted papilloma in a nasal cavity based on a CT image of a nasal cavity. The method for positioning the base of the inverted papilloma in the nasal cavity based on the CT image of the nasal cavity comprises the following steps: acquiring image information of a patient to be predicted; acquiring a trained positioning model; inputting the image information of the patient to be predicted into the trained positioning model, so as to acquire a segmentation mask. The positioning model provided in the application can realize high-precision segmentation and positioning of an IP base region in a CT image, improve the ability of the model in multi-scale heterogeneous structure identification, key region focusing and noise robustness, and further provide reliable technical support for preoperative evaluation, surgical path planning and postoperative efficacy monitoring.

A method of tailoring a spinal implant to correspond to a specific patient's needs includes: pre-operatively evaluating a patient to determine a desired spinal segment response; and modifying one or more features of flexures of an implant to provide the desired spinal segment response. Modifying one or more features of flexures of the implant can include modifying one or more of a thickness, width, length and / or shape of the features of the flexures. Various systems for executing the methodologies taught herein are also provided.

This invention provides an artificial intelligence-based multimodal preoperative assessment method and system for cochlear implantation, relating to the field of artificial intelligence medical technology. The method includes extracting multimodal medical image features, identifying and describing anatomical variations, constructing a three-dimensional anatomical structure, and annotating key information. Based on this, candidate implantation paths are generated. Through spatial sampling and sequence modeling, the cumulative risk during surgical instrument advancement is dynamically predicted, generating a time-series risk curve and multidimensional risk assessment features, ultimately determining the preoperative risk assessment result. This invention achieves dynamic, quantitative, and prospective assessment of cochlear implantation surgery risks.

The invention relates to the technical field of medical artificial intelligence and clinical decision support of gynaecology and obstetrics, in particular to a prognosis analysis method for a cesarean scar pregnant patient, and solves the problems that existing preoperative evaluation depends on doctor experience or a single ultrasonic index, and multi-dimensional clinical data is difficult to integrate for accurate risk prediction. The method comprises the following steps: collecting clinical basic information, ultrasonic image indexes and laboratory inspection data of a cesarean section scar pregnancy patient; performing standardized preprocessing on the data; constructing a feature set by using the training set, and screening key features through random forest or logistic regression; training a machine learning model based on the screening features and carrying out cross validation on optimization parameters; and storing the model, evaluating the performance through an external verification set, and outputting a quantitative operation risk prediction result. According to the method, multi-source data fusion and risk quantitative evaluation are realized, the prediction accuracy and preoperative decision scientificity are remarkably improved, high-risk patients are effectively identified, and making of an individualized operation scheme is assisted.

The invention discloses a biomarker for auxiliary diagnosis or evaluation of epilepsy related to nodular sclerosis and application of the biomarker. The biomarker comprises any one or a combination of at least two of a complement component 7, N-acetylgalactosamine transferase 3, an ecological virus integration site 5, a hemoglobin subunit theta 1 or leukotriene A4 hydrolase. The invention provides a protein biomarker combination which is low in invasiveness and easy to quantify and can be used for preoperative evaluation, postoperative prognosis judgment and curative effect monitoring and a detection method thereof, and the accuracy and reliability of nodular sclerosis related epilepsy diagnosis and risk evaluation are remarkably improved.

The invention relates to the technical field of medical cosmetology, and discloses a small-range scar fine repair skin flap transfer and reconstruction system based on a trephine skin grafting technology, which comprises a preoperative evaluation module, a trephine equipment module, a skin flap processing module, a transfer and reconstruction module, a postoperative management module and a safety monitoring module, the preoperative evaluation module comprises a three-dimensional scar scanning unit, a blood supply analysis unit and a positioning navigation unit, and integrates the preoperative evaluation module, a trephine equipment module, a flap processing module, a transfer reconstruction module, a postoperative management module and a safety monitoring module; the small-range scar fine repair skin flap transfer and reconstruction system of the trephine skin grafting technology has high automation, intelligence and precision, the operation efficiency and the success rate are remarkably improved, and a complete scar repair process is formed.