142 results about "Clinical value" patented technology

The invention discloses a preoperative and postoperative full-process optimization nursing system for the thoracic surgery department, relates to the technical field of medical nursing, and aims to solve the problem that physical execution nursing and information management decision are separated in the prior art; and the nursing intervention is lack of real-time response and closed-loop regulation and control capability to the highly dynamic physiological state of the thoracic surgery patient in the perioperative period. The nursing system is characterized by comprising a multi-mode physiological and environmental perception layer, a dynamic nursing decision engine and a closed-loop physical intervention execution layer. Wherein the sensing layer is responsible for data acquisition, state model construction, risk prediction and optimal intervention planning by a decision engine, and the execution layer accurately implements intervention and feeds back. By the adoption of the technical scheme, individualized, dynamic and prospective management of perioperative nursing of the patient in the thoracic surgery department can be achieved, a refined, individualized and intelligent management means is provided, and the clinical value is remarkable.

The invention relates to the field of artificial intelligence, in particular to an endoscopic image description report generation method and device and a medium. According to the method, a multi-modal endoscope model comprising a visual encoder, a large language model and a cross-modal alignment module is constructed, image analysis and text generation are combined, integrated processing from image recognition to text description is achieved on an endoscope image, a description report is finally generated, and therefore efficient conversion from the image to the text is achieved; the speed and the automation degree of endoscopic image analysis are improved, and the time consumption of a doctor on report writing is reduced. According to the multi-modal endoscope model, the complex medical knowledge and the image analysis result are effectively combined, so that diagnosis suggestions with clinical value can be more accurately generated, namely, a more accurate description report is generated. The multi-modal endoscope model is trained by using a multi-task data set, the capabilities of different tasks can be integrated, and the prediction of a multi-task result is realized on the same endoscope picture.

The invention provides a nervous tissue identification method based on artificial intelligence, and relates to the technical field of medical intelligent detection. According to the nervous tissue identification method, nervous tissue region annotation is carried out on 452 HE-dyed section images of bladder cancer, pancreatic cancer and porta-bile duct cancer by using QuPath software. 70% is used as a training set, 10% is used as a verification set of a training process, and 20% is used as an independent test set to train the nnUnet model. The whole training process is combined with an active learning strategy to enhance the specificity of the model. By using the algorithm and the deep learning technology, the nerve peripheral infiltration in the pathology is accurately and quickly evaluated, the workload of manual identification is greatly reduced, and the diagnosis accuracy is improved. The machine identification of tumor peripheral nerve invasion has obvious practicability, importance, clinical diversity and actual clinical value, and shows that the deep learning technology improves the potential of tumor detection and diagnosis.

The invention discloses a medical institution medical consumable access selection evaluation method and system, and relates to the technical field of medical systems. Comprising the following steps: responding to an evaluation request of a target consumable, generating an evaluation task according to an evaluation index and an evaluation object, and distributing the evaluation task to the corresponding evaluation object; the evaluation indexes comprise a clinical value, an economic value, a patient value and a management value, and performing importance scoring on each index to obtain a corresponding weight of each evaluation index; collecting scoring results of all the evaluation objects on the target consumable, and performing linear weighting on the scoring results of the evaluation indexes according to the corresponding weights to obtain an evaluation result of the target consumable; by constructing a multi-dimensional dynamic evaluation system, an intelligent data fusion mechanism and an automatic task distribution mechanism, the manual process cycle is greatly shortened, the limitation of traditional single-dimensional evaluation is broken through, a linear weighted model is utilized to fuse multi-dimensional scoring results, the scientificity and efficiency of medical consumable admission selection are improved, and the clinical application quality of consumables is improved.

The invention relates to the technical field of cardiovascular examination, in particular to a cardiovascular ultrasonic examination quality control method and system based on artificial intelligence, and the method comprises the following steps: obtaining a valve motion image through ultrasound, extracting a valve leaflet edge coordinate, recognizing a valve opening center and a valve leaflet tip through a convolutional neural network, and generating motion data; the method comprises the following steps: calculating a deviation degree based on trajectory fitting, judging stability, analyzing peak values of a systolic period and a diastolic period, calculating a symmetry difference, extracting a valve opening area, carrying out self-encoder analysis in combination with the symmetry difference to generate a fluctuation amplitude, and carrying out normalization processing to complete hierarchical classification so as to generate a quality control result. By acquiring leaflet edge points and combining a convolutional neural network to identify key features, accurate extraction of motion data, finer trajectory fitting and offset calculation, more scientific quantitative evaluation of symmetry difference and more stereoscopic area fluctuation and feature fusion analysis are realized, hierarchical and hierarchical synchronization is realized through normalization processing, and the accuracy of the motion data is improved. And the result is more credible, more stable and higher in clinical value.

The invention relates to the field of intelligent medical treatment, in particular to an obstructive sleep apnea syndrome detection method and device based on multi-modal data and a program product. Comprising the following steps: S1, acquiring time sequence frame data of infrared thermal imaging of a tester in real time; s2, extracting chest and abdomen position data and air temperature data of a nose and mouth area based on the infrared thermal imaging time sequence frame data; s3, calculating to obtain breathing waveform data through thoracic and abdominal position data in the time sequence frame data; s4, air temperature change curve data are obtained through air temperature data calculation in the time sequence frame data; s5, performing phase correlation on the breathing waveform data and the airflow temperature change curve data to calculate a breathing ratio; comparing the respiration ratio with a preset threshold value, and judging that the obstructive sleep apnea syndrome occurs when the respiration ratio is greater than the preset threshold value. The method can effectively identify the obstructive sleep apnea syndrome, and has a good clinical value.

The invention discloses a structured information extraction system for a mental disorder electronic medical record based on a knowledge graph and a natural language, and belongs to the technical field of medical information processing. According to the system, firstly, core concepts such as diseases, symptoms and drugs are extracted from an authoritative guide to construct a mental disorder knowledge graph, and a standardized clinical knowledge base is established; then pre-training and fine-tuning the deep learning model on a large number of biomedical texts and desensitized medical records to enable the deep learning model to have a medical language understanding ability; performing preprocessing, named entity recognition and entity linking on the electronic medical record text, and mapping spoken expressions to standard medical terms; inference is carried out by utilizing a relation extraction model and combining with a knowledge graph to complement implicit clinical information; and finally, structured data output meeting the standards of FHIR and the like is generated. According to the method, through deep fusion of knowledge driving and data driving, the problems of insufficient semantic understanding and weak generalization ability of a traditional method are effectively solved, and the accuracy and clinical value of electronic medical record structured information extraction are remarkably improved.

The invention relates to the field of intelligent medical treatment, in particular to a traditional Chinese and western medicine multi-modal data association network construction method and device and a program product. Comprising the steps of S1, acquiring a traditional Chinese medicine inquiry data set and a western medicine clinical data set of a patient; s2, performing first feature extraction on the traditional Chinese medicine inquiry data set to obtain N time sequence traditional Chinese medicine features X1, wherein N is the number of doctor seeing times; s3, performing second feature extraction on the western medicine clinical data set to obtain N time sequence western medicine features Y1; s4, the correlation between the Mth time sequence traditional Chinese medicine feature and the Mth time sequence western medicine feature is calculated to obtain a traditional Chinese medicine related feature X2 and a western medicine related feature Y2 which have an incidence relation, M is smaller than or equal to N, Y2 is smaller than or equal to Y1, and X2 is smaller than or equal to X1; and S5, taking the Western medicine related features as labels, obtaining the traditional Chinese medicine related features corresponding to each Western medicine related feature based on the association relationship, and generating an N * Y2 Western medicine related feature-traditional Chinese medicine related feature association network. The application has good clinical value.

The invention discloses a liver and gall part segmentation method and device based on deep learning, equipment and a medium, and belongs to the technical field of artificial intelligence. In the embodiment of the invention, the three deep learning models are used to carry out three-stage image segmentation on the original image containing the liver and gall part, and the position of each human tissue structure of the liver and gall part in the medical image is automatically identified and segmented. Surgical navigation is provided for clinicians during hepatobiliary disease and surgical planning, the deep learning technology is applied, the method is more accurate and faster, the method can be suitable for disease diagnosis, surgical formulation and radiotherapy plans of clinicians in more departments, the applicability of the method is improved, and the method has general clinical value and is worthy of popularization and application. The three-stage segmentation module can realize more accurate segmentation, can give consideration to global information and local information, better conforms to the reading habit of clinicians, and improves the segmentation precision and accuracy.

The invention relates to a keratoconus diagnosis model construction method and system, and the method comprises the steps: collecting corneal topographic map data of two eyes, and determining corneal measurement features in the corneal topographic map data; preprocessing to obtain a sample set; dividing a training set and a test set; training the training set; respectively inputting the training set with the convergent loss function into a plurality of single models for training, and fusing the plurality of trained single models to obtain a fusion model; performing hyper-parameter optimization on the fusion model; retraining the fusion model; evaluating performance indexes of the fusion model; and determining a final diagnosis model, obtaining a definite diagnosis symptom, and outputting a diagnosis result. The method has great innovation in the aspects of feature extraction and fusion, and can be used for diagnosing the early screening of the keratoconus; the diagnosis method has high robustness and accuracy, is convenient to use, efficient and convenient, saves time and labor in the diagnosis process, can avoid the misdiagnosis rate, effectively improves the diagnosis and treatment efficiency, and has important clinical value and significance.

The invention belongs to the technical field of medical image processing, and particularly relates to a left-hand X-ray image double-stage bone age evaluation system based on cross-modal learning. The system comprises a data preparation module, a coarse granularity classification module, a fine granularity regression module and an output module. The method comprises the following steps: firstly, preprocessing a hand X-ray image, and constructing cross-modal feature input in combination with structured text description of a standard bone age map; through an SBA-CLIP + cross-modal learning model, alignment and fusion of image features and text features are realized, and coarse-grained classification of bone age intervals is completed; and according to a classification result, dynamically calling a fine classification sub-model of a corresponding interval, and realizing fine regression prediction of the bone age. In the process, a bimodal attention alignment module is introduced to enhance internal feature representation of images and texts, and cross-modal alignment and classification are improved in combination with a comparison loss function guided by cyclic consistency. According to the method, the prediction precision and interpretability are remarkably improved, and the method has wide application prospects and clinical value.

The invention discloses a cognitive impairment assessment system and method based on virtual reality, and relates to the technical field of virtual reality, and the method comprises the steps: constructing a plurality of space navigation tasks with different complexities in a preset virtual environment, collecting paths selected by a patient in the process of executing the space navigation tasks and related behavior data, analyzing the behavior data to identify a spatial navigation mode and deviation characteristics of the patient, and regulating and controlling path selection parameters of the patient in a subsequent task based on an identification result to improve evaluation precision; according to the cognitive impairment assessment system and method based on virtual reality, targeted diagnosis suggestions are provided for medical personnel, the assessment efficiency and clinical value are improved, the assessment requirements of different patients and different cognitive impairment types can be met, and the application range and flexibility of the system are expanded.

The invention discloses a risk prediction method for patients with Parkinson's dysphagia, and relates to the technical field of medical health. The method comprises the steps that S1, a multi-modal data fusion framework is built, the multi-modal data fusion framework aims at effectively fusing biomarkers, dynamic swallowing kinematics characteristics and clinical data, the core of the multi-modal data fusion framework comprises characteristic alignment, inter-modal interaction modeling and fusion decision, and a first risk prediction probability is output; through a multi-modal data fusion algorithm, a dynamic swallowing function quantification technology and a lightweight edge deployment scheme, high-precision prediction, early warning and personalized intervention of the Parkinson's dysphagia risk are realized, the method has remarkable clinical value, technical barriers and market competitiveness, and the core demand of medical productization landing is met.

The invention provides an automatic segmentation and region division imaging method and system for ultrasonic velocity estimation, a storage medium and electronic equipment, and the method comprises the steps: dividing an ultrasonic B image into a plurality of tissue regions based on an edge recognition result; obtaining channel data in the ultrasonic imaging process; based on the sound velocity range, performing beam forming according to the channel data; based on the reconstructed image at each wave velocity, according to each tissue area, performing focusing quality evaluation; determining the optimal sound velocity of each tissue area based on the focusing quality evaluation result of each tissue area; based on the optimal sound velocity of each tissue area, performing color coding mapping on each tissue area to obtain a sound velocity color image of each tissue area; and superposing the sound velocity color image of each tissue area with the ultrasonic B image to obtain a final ultrasonic sound velocity image. Through automatic tissue segmentation and region division, in combination with accurate sound velocity estimation and visual sound velocity color coding mapping, the method has a wide application prospect and an important clinical value.

The invention relates to the technical field of medicine, and provides a medicine clinical value evaluation method and system, electronic equipment and a storage medium, and the system comprises a medicine information collection module which is used for obtaining first information of a to-be-evaluated target medicine; the evaluation standard construction module is used for setting an evaluation standard based on a preset evaluation requirement; the standard quantitative assignment module is used for performing weight assignment on the evaluation standard; the evaluation evidence integration module is used for acquiring second information of the target drug based on the evaluation standard set by the evaluation standard construction module and the first information; and the drug value evaluation module is used for evaluating the clinical value of the target drug based on the second information of the target drug by applying an evaluation standard. The method and the device are used for overcoming the defects of dimensionality stiffness, quantization distortion, evidence dispersion and decision lag during evaluation of the clinical value of the medicine in related technologies. The method and the device can be used for realizing scientific evaluation, dynamic management and scenarized decision of the clinical value of the medicine.

The invention discloses an ERCP postoperative common bile duct calculus recurrence prediction system based on machine learning, and the system comprises the following steps: a data collection module which is used for forming an original clinical data set; the data preprocessing module is used for constructing a standardized preprocessed clinical data set; the feature construction and hyper-parameter search space definition module is used for defining a hyper-parameter search space of the improved bidirectional gating loop unit network model; the improved bidirectional gating circulation unit network model construction module is used for outputting a recurrence risk prediction value; the grey wolf optimization hyper-parameter tuning module is used for finally generating an optimized improved bidirectional gating circulation unit network model; the model training and prediction module is used for constructing a risk classification rule according to the prediction probability and the postoperative key indexes; and the individualized result output module is used for outputting the prediction result and pushing the prediction result to a doctor end. The method has significant clinical value in early recognition of high-risk individuals in actual deployment.

The invention discloses a construction method of a detection system for an HLA cross reaction group and a kit. The HLA cross reaction groups are grouped, the primers and the specific probes are designed, a reasonable detection system is constructed, the groups of the cross reaction groups can be directly detected instead of independently detecting each subtype in the groups, so that the number of the required primers and probes is greatly reduced, the detection efficiency is improved, and the cost is reduced. Combined interpretation is carried out by means of multiple fluorescence channels and melting curves, grouping information of samples can be obtained only through three reaction systems, and the method has the advantages of being rapid, easy and convenient to operate, high in flux and the like and has wide application prospects and clinical value in the fields of clinical blood transfusion medicine, organ transplantation and the like.

Systems and methods for establishing a patient's current or future clinical or lab values are provided. A neural network is trained on a dataset of medical images, such as ultrasound images, that are tagged with information concerning the lab values of people who were imaged to produce the medical images. The trained neural network can then be provided with medical images of a patient, and the neural network can then make a determination as to the patient's current or future clinical or lab values.

The invention provides a benefit-risk evaluation method and a technical system for Chinese patent medicines after marketing. The method aims at solving the limitation of an existing medicine benefit-risk evaluation method in evaluating the Chinese patent medicine and comprehensively reflecting the benefit and the risk of the Chinese patent medicine in clinical application. A perfect benefit-risk index system is constructed by adopting a qualitative and quantitative combined evaluation mode, and factors such as multiple indications and treatment based on syndrome differentiation of the Chinese patent medicine are fully considered. According to the technical scheme, the method comprises the following steps: a benefit-risk evaluation strategy after the traditional Chinese medicine is marketed, qualitative and quantitative benefit-risk evaluation, and establishment and implementation of a key technology. According to the method, the value of the Chinese patent medicine in clinical treatment can be evaluated more scientifically and comprehensively, powerful support is provided for clinical value evaluation of the Chinese patent medicine, and the accuracy and reliability of evaluation results are improved. The implementation of the method provides a reference basis for clinical decision making.

The invention discloses a radiotherapy plan automatic optimization method and device based on a large language model and RAG, and the device comprises an initial plan generation module which is used for generating an initial scheme based on a fixed weight; the data construction module is used for establishing a clinical knowledge base and completing semantic segmentation and vector indexing; the retrieval module is used for matching the query vector with a knowledge base, and obtaining a weighted knowledge set through similarity screening and reordering; the enhancement prompt generation module is used for generating an enhancement prompt; the large language model evaluation module is used for evaluating a scheme and outputting a weight adjustment suggestion; the iterative optimization module is used for updating parameters and generating a new scheme until convergence; and the comprehensive evaluation module analyzes the candidate schemes and outputs an optimal radiotherapy plan. According to the method, the large language model and the RAG are combined, clinical knowledge dynamic introduction and weight adaptive optimization are realized, manual intervention is reduced, target coverage and endangered organ protection are both considered, and radiotherapy plan intelligence and clinical value are improved.

The invention belongs to the field of intelligent medical treatment, and particularly relates to a method, equipment, medium and program product for predicting RMPP based on lung microvascular changes. The method comprises the following steps: S101, acquiring a CT image of a pediatric MPP patient; s102, the CT image is extracted to obtain a quantitative PBV parameter, the quantitative PBV parameter comprises BV10%, and BV10% refers to the percentage of the blood volume in a blood vessel with the blood vessel cross section area larger than 10 mm < 2 > in the total lung blood volume; and S103, if BV10% is higher than a first threshold value, judging that the pediatric MPP patient develops into a prediction result with high RMPP risk, and otherwise, outputting a prediction result with low RMPP risk. According to the application, the prediction effect on the occurrence and development of the RMPP is found by quantitatively measuring the microvascular change in the CT, and the clinical value of early diagnosis of the RMPP is realized, so that the clinical early treatment is driven.

The embodiment of the invention discloses a gynecological malignant tumor postoperative home nursing and system. The method comprises the following steps: determining patient information corresponding to a patient to be nursed, wherein the patient information comprises physiological data and malignant tumor postoperative data; determining a real-time health state of the to-be-nursed patient based on the physiological data and the malignant tumor postoperative data; inputting the real-time health state into a preset nursing method atlas database, and determining a home nursing method corresponding to the patient to be nursed based on the preset nursing method atlas database; and obtaining personalized data of the to-be-nursed patient, and correcting the home nursing method based on the personalized data to obtain a target home nursing method corresponding to the to-be-nursed patient. According to the application, the scientificity, safety and patient compliance of home care can be remarkably improved, and rehabilitation support which has higher clinical value and meets individual requirements is provided for malignant tumor postoperative patients.

The invention relates to the technical field of health monitoring, in particular to a foot multi-area temperature and humidity cooperative monitoring system and method for diabetic foot ulcer risk prediction.The intelligent sensing terminal is integrated with a distributed temperature and humidity sensor array, a motion sensing unit, a microprocessor and a first wireless communication module through a flexible circuit; the mobile relay terminal is internally provided with a second wireless communication module and a terminal application program, the cloud analysis platform is deployed with a time sequence database, an artificial intelligence analysis engine and a message push service, and the medical interaction terminal provides a visual interface for medical staff and patients. According to the system and the method, real early warning is realized, the accuracy and the reliability of risk assessment are improved, a complete collaborative closed-loop system is constructed, efficient energy management and excellent user experience are realized, long-term self-optimization and self-adaptive evolution capabilities are also realized, and the clinical value of the system and the method is continuously improved.

The invention discloses a construction method of an AIS patient pre-hospital delay risk prediction model, and relates to the field of medicine. Comprising the following steps: data acquisition: acquiring data through a hospital electronic medical record system and telephone return visit, and performing anonymization processing on the data after approval by an ethical committee; model construction: performing inter-group comparison by using SPSS 29.0, then screening independent risk factors of pre-hospital delay by using binary Logistic regression, and finally constructing a column diagram prediction model by using an rms packet of R 3.6.3 software; and verifying the model, and evaluating the distinction degree through the area AUC under the working characteristic curve of the subject. The pre-hospital delay prediction model for the first-onset AIS patient, constructed by the method, has good distinction degree and accuracy, provides a basis for identifying the pre-hospital delay of the first-onset AIS patient, effectively shortens the arrival time of the patient for the first-onset AIS patient, and has important public health significance and clinical value.

The invention relates to the field of intelligent medical treatment, in particular to a colposcope image-based data processing and segmentation method, equipment and a program product. The method includes: acquiring a vagina vinegar white image; performing mirror surface area detection on the vagina vinegar white image to obtain a mirror surface area; in the mirror surface area detection, a vagina vinegar white image is converted into an HIS color space to obtain a low-saturation S, high-brightness I and gradient GR three-channel vagina vinegar white image, and a mirror surface area is marked based on the three-channel vagina vinegar white image; and performing priority calculation on each pixel point in the mirror surface area, and performing patch repair processing based on a priority sequence to obtain a processed vagina vinegar white image. The application has good clinical value.

The invention relates to the field of intelligent medical treatment, in particular to a continuous time sequence CT image construction method and device based on EIT and a program product. Comprising the steps of performing CT scanning on a to-be-detected part and a to-be-detected person, and obtaining CT images of the to-be-detected person at the end of inspiration and the end of expiration and EIT impedance images of the same continuous time period from the end of inspiration to the end of expiration; the CT image at the end of inspiration and the CT image at the end of respiration are used as a first frame and a tail frame respectively, and a motion field for converting the CT image at the end of inspiration into the CT image at the end of expiration is predicted based on the dynamic change of the EIT impedance image in a continuous time period from the end of inspiration to the end of expiration; registering the motion field, the first frame and the tail frame to obtain a registered motion field, a registered first frame and a registered tail frame; and generating a CT image in a middle time period through the registered motion field, the first frame and the tail frame, and obtaining a CT image in a continuous time period from the end of inspiration to the end of expiration. The application has good clinical value.

The application discloses a kind of based on multi-source medical text construction traditional Chinese medicine diabetic nephropathy knowledge graph method and system, including following module: data integration module: integration diagnosis and treatment guideline and electronic medical record, after redundancy, terminology standardization constructs unified medical data set;Mixed labeling and entity recognition module: fusion artificial labeling and BERT-BiLSTM-CRF model automatically identifies traditional Chinese medical syndrome, traditional Chinese medicine and other complex entities;Relationship modeling module is based on semantic embedding and multi-label classification algorithm, accurately constructs traditional Chinese and western medicine association rules;Atlas application module: entity relationship is converted into attribute graph structure, supports graph database interactive query, realizes diagnosis and treatment recommendation, contraindication early warning and curative effect analysis.Through the above scheme, the present application significantly improves the accuracy of differentiation, reduces the risk of drug contraindication, provides efficient, interpretable decision support for the prevention and treatment of diabetic nephropathy, has important clinical value and popularization potential.

The invention relates to the field of intelligent medical treatment, in particular to an allergy data processing method and device and a program product. Comprising the steps of obtaining a basic data set and a risk category label of a to-be-tested person; performing data expansion on the data set and the label of the risk category through a generative adversarial network to obtain expanded data; in the generative adversarial network training process, a generator generates preliminary synthetic data by using random noise and feature information of original allergy-related medical data, a discriminator distinguishes real allergy-related medical data from synthetic allergy-related medical data, and iterative training is performed on the generator and the discriminator; loss function calculation of the generative adversarial network generator comprises adversarial loss and feature comparison loss, calculation of the adversarial loss comprises random noise data and feature data of the allergic medical data, and calculation of the feature comparison loss comprises the allergic medical data. The application has good clinical value.

The invention relates to the field of intelligent medical treatment, in particular to a breast symmetry evaluation method based on deep learning and a program product. Comprising the steps of S1, acquiring a trunk image; s2, performing breast region segmentation on the trunk image to obtain segmented images; s3, calculating the area of a left breast region and a right breast region based on the segmented image; s4, performing depth information extraction on the trunk image to obtain a depth information image; s5, calculating the volumes of the left and right breasts based on the left and right breast area and depth information images; and S6, comparing the absolute value difference of the areas of the left and right breasts with the absolute value difference of the volumes to obtain an evaluation result of the symmetry of the breasts. According to the method, the breast features can be automatically and quickly identified, the symmetry of the breasts can be evaluated, and the method has a good clinical value.

The invention relates to the field of intelligent medical treatment, in particular to a pulmonary nodule segmentation model construction method and device based on traditional Chinese medicine intervention and a program product. The method includes: acquiring pulmonary nodule image data sets of a patient before and after traditional Chinese medicine intervention; the pulmonary nodule image data sets before and after traditional Chinese medicine intervention are transmitted to a segmentation model to be trained for training until a loss function is unchanged, and a pulmonary nodule segmentation model is obtained; the segmentation model sequentially comprises a convolution layer, an encoding module, a decoding module, a second convolution layer and an output layer, the encoding module comprises N encoding layers, each encoding layer comprises a positioning feature extraction layer and a residual module which are connected in series, the decoding module comprises M decoding layers, image data obtains positioning features through the convolution layers and the N encoding modules, and the N is a positive integer; and the positioning features sequentially pass through the deconvolution layer and the decoding layer and then are output through the output layer. The application has good clinical value.