162 results about "Medical training" patented technology

The invention specifically discloses a triage training system and method based on a large language model and a knowledge graph, and relates to the technical field of medical training. The system comprises a multi-modal data sensing and processing module which is used for accessing and processing diversified training data sources; the dynamic time sequence knowledge graph construction and updating module comprises an entity relationship extraction unit, a multi-modal alignment unit and a knowledge fusion and conflict resolution unit; the multi-agent collaborative service module is composed of large language model agents which are special in function and work cooperatively, and comprises an extraction agent, a construction agent, a reasoning agent and a teaching agent; and the immersive interactive training and feedback module comprises a virtual case generation unit, a multi-modal interactive interface and a deep feedback system. According to the invention, the authenticity, accuracy and effectiveness of training are significantly improved, it is ensured that the learned knowledge and skills can efficiently adapt to a real emergency treatment scene, and the accuracy and efficiency of triage classification in emergency treatment are improved.

The invention relates to a medical training analog simulation system based on big data analysis, in particular to the field of medical training analog simulation, the medical training analog simulation system provides an efficient, accurate and dynamically adaptive training environment through cooperative work of a plurality of modules; the data processing module ensures high quality of input data, the construction module improves the generalization ability of the model through transfer learning and multi-task learning, the dynamic optimization module improves the model prediction precision through self-supervised learning and learning rate adjustment, and the real-time feedback module monitors student operation and provides personalized tutoring; the whole system combines high-quality data, intelligent training and real-time feedback, provides accurate and personalized support for medical education and practical training, and improves operation skills and coping ability of students.

System for observing medical conditions and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive, from a data repository, a plurality of reference electronic health records and a plurality of reference cardiac data elements, generate medical training data, train at least an observation machine learning model using the generated medical training data, receive a query pertaining to a subject, wherein the query includes at least a query cardiac data element and at least a query electronic health record, and output at least an observation outcome as a function of the query using the at least an observation machine learning model.

The invention discloses a medical training evaluation method and system based on an intelligent simulation patient, and relates to the field of artificial intelligence technology and medical simulation, and the method comprises a virtual patient model construction step, a multi-modal perception step, an intelligent interaction step and an intelligent evaluation step. By integrating voice recognition, natural language understanding, facial expression recognition, gesture recognition and virtual reality technologies, a high-simulation and high-interactivity intelligent patient model is constructed for medical students or clinical medical staff to perform diagnosis and treatment training and skill operation assessment. The virtual patient model supports custom information such as age, gender, race, medical history, living habits and the like, different virtual patient images and different pathophysiological states are generated, and personalized learning requirements of users are met. In addition, targeted training suggestions are provided, the training difficulty and content can be adjusted according to the performance of the user, and different training requirements are met.

The invention discloses an insurance evaluation large model training method and device, equipment and a storage medium, and relates to the technical field of data processing. Comprising the steps of performing data compliance processing on medical training data and financial training data provided by participants participating in model training through a federal learning architecture in a preset distributed data management component to obtain compliance data; reading compliance data from a preset distributed data management component based on a preset data fusion engine, and performing fusion processing to obtain cross-domain fusion data; obtaining cross-domain fusion data by using a preset large model joint training framework, and generating a target model training task based on the model training capability corresponding to each participant; and training the to-be-trained model according to the target model training task and the cross-domain fusion data to obtain a target insurance evaluation large model so as to perform medical insurance information evaluation based on the target insurance evaluation large model. In this way, efficient retrieval and compliance sharing of medical and financial cross-domain data resources can be achieved.

The invention provides a digital wounded dynamic evolution method and device based on limb fusion and a medium, and relates to the technical field of medical training. The method comprises the following steps: receiving a digital wounded generation instruction; determining a plurality of target limbs and a plurality of target dynamic response models based on the digital wounded generation instruction; wherein at least one target limb in the multiple target limbs has an injury condition, and at least one target limb corresponds to at least one injury condition type; carrying out limb fusion on the plurality of target limbs to obtain an initial digital wounded person; according to the digital wounded person generation instruction and a plurality of target dynamic response models, enabling each target limb in the initial digital wounded person to perform associated response to obtain a target digital wounded person; and performing dynamic evolution on the injury condition of the target digital wounded person based on the rescue operation performed by the testee on the target digital wounded person. According to the invention, the simulation precision of the digital wounded can be improved, the dynamic evolution process of the injury condition can be accurately displayed, and abundant training is provided for the testee.

The invention relates to the technical field of medical training, and discloses a training management method for improving nurse nursing information competency, comprising the following steps: S1, through investigation and demand analysis, S2, through cloud computing and big data technologies, S3, introducing a high-fidelity simulation device into a physical simulation module, S4, establishing a dynamically updated case library, and S5, establishing a dynamic update case library; s5, constructing a physical simulation module, S6, organizing teacher special training and a remote tutoring mechanism with an apprentier, S7, introducing an artificial intelligence-based personalized learning recommendation system into a training platform, and S8, combining the artificial intelligence recommendation system with real-time data provided by the physical simulation module. Through special training, nurses not only can grasp theoretical knowledge in a simulated environment, but also can improve the ability to deal with complex clinical conditions through situation drilling and actual operation, and the nurses can deeply understand nursing theories and improve theoretical support and practical knowledge of the nurses in the training process.

The invention relates to the technical field of medical training equipment, in particular to a critical care ultrasonic image training system, which comprises a clinical data acquisition module for acquiring critical clinical case data, ultrasonic image materials and diagnosis and treatment process specifications; the training scene construction module is used for constructing a high-fidelity three-dimensional training scene according to severe clinical case data, ultrasonic image materials and diagnosis and treatment process specifications; the virtual ultrasonic practical operation module simulates the operation of an ultrasonic probe based on a high-fidelity three-dimensional training scene, and generates an ultrasonic image corresponding to an anatomical part in real time; the intelligent skill assessment center assesses operation normalization and diagnosis accuracy through practical operation data and real-time generation of an ultrasonic image corresponding to an anatomical site in combination with an improved ResNet-LSTM model, and an assessment result is obtained; and the personalized teaching unit generates a customized training scheme and knowledge strengthening content according to the evaluation result in combination with the learning track of the student. Therefore, the problems of insufficient dynamic scene simulation, poor authenticity and the like in the prior art are solved.

The invention provides a medical anthropomorphic dummy training system, and relates to the technical field of medical treatment. The medical dummy training system comprises a user interface layer and is characterized in that the user interface layer is connected with a training module layer, the training module layer is connected with a data management layer, and the data management layer is connected with a technical support layer; the user interface layer comprises a login / registration module, a user instrument panel and virtual character creation, the training module layer comprises a situation simulation module, a decision tree module, a collaborative task module and a case redisk module, and the data management layer comprises user data management, training content management and feedback and evaluation management. The technical support layer comprises a situation engine, a decision tree algorithm and a data analysis tool. According to the online training system, efficient, flexible and immersive medical training experience is provided for the user, and the clinical skill and decision level are comprehensively improved.

The invention provides a medical question and answer method fusing cross-modal hybrid experts, and the method comprises the steps: obtaining a medical image and a question text, and carrying out the preprocessing of the medical image and the question text, and obtaining an initial visual embedding matrix and an initial text embedding matrix; inputting the initial visual embedding matrix and the initial text embedding matrix into a trained medical question and answer model to obtain an answer text for the medical image and the question text; the medical question and answer model comprises a visual encoder, a text encoder, a position encoding module, a feature fusion module, a gating router, a hybrid expert module and an answer generator. A cross-modal Transform is utilized to realize deep semantic alignment of image and text features, a plurality of expert networks are dynamically scheduled through a gating router, specialized processing is performed for different feature vectors, and the adaptability of the model to complex lesions and rare cases is enhanced; multi-angle reasoning is carried out, accurate and credible answers are generated, and an efficient and reliable solution is provided for auxiliary diagnosis, medical training and scientific research analysis.

The invention belongs to the technical field of medical training, and discloses an operation training system and method based on multi-dimensional data perception fusion, and the system comprises an image collection module, a six-degree-of-freedom operation mechanism, a somatosensory and tactile feedback glove, an operation training robot, a simulation heart model, and a control system. The image acquisition module is in signal connection with the control system and is used for acquiring a 3D high-definition image and instrument space coordinates of an operation area in real time; and the six-degree-of-freedom operating mechanism is in signal connection with the control system and is used for converting an operation instruction of a doctor into a control signal of the operation training robot and feeding back force sense information. According to the scheme, a high-simulation operation training environment is provided through the six-degree-of-freedom teleoperation mechanism, the somatosensory and tactile sensing gloves, the operation training robot and the simulation heart model made of the multi-dimensional sensing electronic skin, multi-dimensional recording and feedback of operation operation are achieved, and the operation training efficiency is improved by combining a transfer learning method. And accurate recording and reproduction of the operation process of the qualified doctor are realized.

The invention belongs to the technical field of medical training, and particularly relates to a method and a system for constructing a virtual operating room resource library for reappearing an offshore operation scene, and the method comprises the following steps: 1, collecting offshore medical data and scene demands: collecting an offshore environment and medical data, and determining medical care core capability demands; in the application, coupling parameters generated by a sea condition dynamic sensing module drive a mixed reality scene rendering module to present real shake and environment change; meanwhile, parameters are synchronously input to the equipment modeling module, fault response of virtual equipment is triggered, a user operates the virtual equipment through the interaction module and executes an operation on a virtual human body model, and the resource library management module guarantees reusability and updatability of resources in the whole process. Through real-time data linkage among the modules, total-factor dynamic coupling of sea conditions, scenes, equipment and operation objects is finally realized, and the problem of simulation distortion of a traditional system is solved.

The present invention relates to a virtual reality (VR) system and method for enhancing medical training. Integrating advanced hand tracking technology with a VR headset, this system offers an immersive training environment for a variety of medical procedures, including CPR, emergency medical interventions, and physical examinations. The hand tracking sensors provide precise monitoring of hand movements, allowing for gesture-based interaction without physical controllers. The system includes several modules for different medical scenarios, each designed to accurately replicate medical techniques and provide real-time feedback. Key innovations include haptic feedback, adjustable difficulty levels, interactive tutorials, and an AI-driven virtual instructor for a personalized training experience. The system is particularly beneficial for training healthcare professionals in critical skills necessary for effective patient care in emergency situations, representing a significant improvement over traditional training methods.

The invention relates to the technical field of medical training, and discloses a sputum suction operation training method and system based on a virtual reality technology. The method comprises the steps that historical operation records of a trainee are collected, key operation nodes are extracted, and an airway state model of a virtual patient is constructed based on the nodes; secondly, setting a virtual sputum suction equipment model, performing a multi-working-condition simulation test on the virtual sputum suction equipment model to obtain operation response characteristics, and mapping the equipment model into the virtual airway state model; then calculating a secretion distribution parameter of a virtual airway in a training period and a theoretical clearance load of virtual equipment, and combining operation response characteristics to obtain an operation intensity parameter; generating an operation triggering instruction sequence based on the operation intensity parameter, and performing staged operation control on the virtual equipment according to the sequence; and finally, carrying out quantitative evaluation on the operation effect of the virtual equipment in the virtual airway. According to the method, a targeted virtual training scene can be constructed, a real operation response is restored, and accurate tracking and effect quantitative evaluation of the operation process are realized.

A customisable medical training system includes a processor and a memory, with the memory containing instructions that cause the processor to generate or modify a virtual environment based on a customisation input The virtual environment comprises a 3D virtual scene displaying a virtual patient, a state of the virtual patient, and a set of one or more available virtual medical resources displayable in the 3D virtual scene. The system communicates with a VR display system to render the generated or modified virtual environment to a training recipient via the VR display system. The customisation input is an input received to customise the state of the virtual patient and / or the set of available virtual medical resources.

The invention discloses a multi-modal classification model training method, a multi-modal classification method and a storage medium, and the method comprises the steps: obtaining a training data set which comprises a medical training image, clinical training information and label information; performing focus area detection on the medical training image to obtain a focus area training image; according to a corresponding coding rule, coding the focus area training image, the clinical training information and the label to obtain an image training feature, a clinical training feature and a label feature; performing feature fusion on the image training features and the clinical training features to obtain fused training features; and based on the fusion training features and the label features, performing iterative training on the multi-modal classification model until a preset convergence condition is met, and obtaining a trained multi-modal classification model. Redundant information interference is reduced, clinical priori knowledge fusion is enhanced, multi-modal information interaction is optimized, and classification reliability and accuracy and model generalization ability are improved.

The invention provides a teaching interaction processing method and system applied to an intelligent online practical training platform. The method comprises the following steps: firstly, acquiring a training scene data set comprising operation process simulation data (corresponding to gynecological examination and related medical skill standard operation processes) and anatomical structure model data (constructed based on medical image data and comprising three-dimensional structure feature information of a female reproductive system), and then carrying out interactive content generation processing on the training scene data set; generating interactive teaching content including operation step guide information and a three-dimensional dissection display model by combining teaching logic rules, and then receiving interactive operation data (corresponding operation simulation behaviors including operation action time sequence records and spatial position coordinates) of a user on a platform; then training effect evaluation processing is carried out on the interactive operation data and the interactive teaching content to generate a training evaluation result, and finally personalized feedback content is generated according to the evaluation result and pushed to a user interface to guide a user to adjust a training behavior, so that the medical training teaching quality and efficiency are improved.

A bidirectional feedback system for remote spatial positioning correction of a robotic arm for ultrasound scanning is disclosed. An embodiment includes: a first robotic arm for ultrasound scanning; a second robotic arm for mirroring the first robotic arm; a first and second display; an electronic data processor configured for: receiving ultrasound scan images; sending the received ultrasound scan images to the two displays; mirroring the relative spatial positions of the first and second robotic arm, wherein: sensing a first relative spatial position from the first robotic arm, and moving the second robotic arm to the first relative spatial position; has higher priority than: sensing a second relative spatial position from the second robotic arm, and moving the first robotic arm to the second relative spatial position. Also disclosed is a respective method and use of said system for remote hands-on training, preferably for medical training, more preferably for ultrasound training.

The utility model discloses a pupil simulation feedback device for cardio-pulmonary resuscitation, and relates to the technical field of medical training equipment, the pupil simulation feedback device comprises a dummy model and two outer frames clamped at the positions of eye sockets, a circuit board is fixedly arranged at the bottom of an inner cavity of each outer frame, and a lifting mechanism is arranged on the upper portion of each circuit board; the lifting mechanism comprises a supporting frame fixedly arranged in the middle of the upper surface of the circuit board, a threaded rod is movably arranged in the middle of an inner cavity of the supporting frame in a penetrating mode, a push-pull block is arranged outside the threaded rod in a threaded penetrating mode, diagonal bracings are rotationally connected to the two sides of the push-pull block, a top plate is rotationally connected to the upper portions of the diagonal bracings, and an LED liquid crystal screen is fixedly arranged in the middle of the upper surface of the top plate. A transparent box cover is fixedly arranged on the upper portion of an inner cavity of the outer frame, and a transparent cover is fixedly arranged in the middle of the upper surface of the transparent box cover. The problems that the recovery condition of a patient can only be judged by observing different lamplight, and it is inconvenient for medical staff to judge the actual condition more visually and clearly are solved.

The invention relates to the technical field of medical training, and discloses a lumbar puncture simulation teaching system based on an AR technology, and the system comprises an AR content generation module which collects a lumbar medical image and carries out the tissue segmentation and focus recognition; performing three-dimensional geometric modeling based on the tissue segmentation result and the focus recognition result to generate a lumbar vertebra 3D model; the AR display interaction module develops an interaction function of the lumbar vertebra 3D model by using a Unity platform and AR equipment; interactive operation of the user is monitored, and feedback is conducted in real time; and the operation evaluation module is used for evaluating the interaction operation of the user and assigning an improvement suggestion according to an evaluation result. A three-dimensional visual scene is constructed through the AR technology, a user can observe the needle inserting process from different angles according to the complex anatomical structure of the lumbar vertebra part, and dynamic effects such as cerebrospinal fluid pressure change are simulated; in the lumbar puncture simulation training, the AR system can feed back operation data in real time, such as the needle inserting angle, the puncture strength and the puncture depth, and the user is helped to accurately master operation skills.

The motion estimation of an anatomical structure may be performed using a machine-learned (ML) model trained based on medical training images of the anatomical structure and corresponding segmentation masks for the anatomical structure. During the training of the ML model, the model may be used to predict a motion field that may indicate a change between a first training image and a second training image, and to transform the first training image and a corresponding first segmentation mask based on the motion field. The parameters of the ML model may then be adjusted to maintain a correspondence between the transformed first training image and the second training image and between the transformed first segmentation mask or a second segmentation mask associated with the second training image. The correspondence may be assessed based on at least a boundary region shared by the anatomical structure and one or more other anatomical structures.

The invention, which relates to the technical field of medical training, discloses a multi-sensory feedback fusion stroke rehabilitation exoskeleton robot training system comprising an exoskeleton robot main body, a multi-sensory feedback module, and a motion state monitoring module. By integrating a multi-sensory feedback module, a visual, auditory, tactile and motion state monitoring module and a central control module, multi-dimensional sensory stimulation and real-time data acquisition are realized when a patient is trained, and the central control module adapts parameters in the module according to the real-time motion precision of the patient and personalization. The strength and the type of multi-sensory feedback are dynamically adjusted, meanwhile, motion parameters of an exoskeleton robot body are controlled, a dynamic adjustment mechanism ensures pertinence and effectiveness of training, forward feedback is provided to strengthen a correct motion mode when the motion precision of a patient is high, and correction prompt is provided and assistance or resistance of the robot is adjusted when the motion deviation is large. Therefore, nerve function remodeling and motor ability recovery are effectively promoted.

The invention discloses a multi-mode tactile feedback VR interaction method and system and a storage medium, and relates to the technical field of virtual reality, and the method comprises the steps: 1, virtual object physical attribute modeling; step 2, interaction event detection and contact state layering; step 3, layered progressive multi-mode signal generation; step 4, cross-modal cooperative compensation is carried out; and step 5, tactile feedback signal output: smoothing the comprehensive tactile feedback signal and then outputting the tactile feedback signal to a multi-mode tactile feedback device. The method can be widely applied to various scenes such as VR game entertainment, virtual shopping experience, remote medical training, industrial design review, virtual cultural relic display and the like; a user can truly perceive the material, temperature, weight and surface characteristics of the virtual object in the scenes, and the application range and practical value of the VR technology are remarkably expanded.

The invention discloses a medical training data quality inspection method, related equipment and a program product, and relates to the technical field of artificial intelligence. When it is determined that the first reasoning result corresponding to the first problem in the medical training data contains error information, the probability distribution of the token level in the first reasoning result is predicted through a large model in the medical field. According to the method, the error reasoning model is configured, the error reasoning model is trained by adopting a problem sample in the medical field and an error reasoning result, the model simulates the token selection preference of error reasoning, and the error reasoning model performs reasoning on the first problem to obtain the probability distribution of the token level in the second reasoning result. According to the method, the two probability distribution differences of the token level can be compared, key information most possibly containing errors in the first reasoning result can be intelligently recognized, the purpose of automatically positioning potential error points in the first reasoning result is achieved, the modification direction is pointed out for data recovery work, and the data recovery work difficulty is reduced.

The invention discloses a newborn rare disease clinical feature auxiliary diagnosis system based on deep learning, relates to the technical field of medical diagnosis, and aims to solve the problem of multi-dimensional high-dimensional data feature fusion, break through the generalization ability limitation of a traditional diagnosis model, accurately capture a hidden feature combination and improve the diagnosis accuracy. The neonatal rare disease diagnosis system comprises a medical database module used for storing medical training samples and medical data of a patient to be diagnosed; the deep learning system is used for carrying out feature extraction, coding, matching and diagnosis on the medical data; the diagnosis result visualization tool comprises a visualization tool and a textualization tool. According to the method, multi-dimensional high-dimensional data are fused through the feature embedding module, the generalization limitation of a traditional diagnosis model is broken through, and hidden feature combinations are accurately captured; the training feature coding module reduces the over-fitting risk, and for complex cases of methylmalonic acidmia, the diagnosis accuracy is greatly improved compared with a traditional method.

The invention belongs to the technical field of endoscopic examination, and discloses an automatic adjusting device for endoscopic examination, which comprises an endoscope main body module, an image acquisition and processing module, a surgical instrument identification module, an automatic focusing module, a visual angle automatic adjusting module, a central control module and an interaction module, the automation function of the system reduces the dependence on assistants, and the operating room resource allocation problem is obviously improved especially under the condition of complex operations or insufficient manpower. Operation images and endoscope adjustment data recorded by the system can be used for postoperative analysis and teaching, and important support is provided for medical training. And by reducing the operation time and the error risk, the system improves the intraoperative safety of the patient, and further promotes the development of the endoscopic operation. Through organic combination of a wide-angle lens, an artificial intelligence recognition algorithm, automatic focusing and a visual angle adjustment technology, an efficient and intelligent automatic solution is provided for endoscopic examination, and multiple key problems in the prior art are solved.

The invention discloses a heterogeneous medical data-oriented global federal learning method, which comprises the following steps of: determining medical institutions participating in cooperative training, and collecting medical training data by the medical institutions to construct a medical training data set; and determining the structure of the medical global model at the server side according to the medical training data set. And the medical institution receives the medical training model issued by the server as a local model for training. And the medical institution calculates a model update amount according to the trained local model and the medical global model, and adjusts parameters of the local model by using the model update amount. After all the medical institutions complete local training and model parameter adjustment, local model parameters are uploaded to the server side. According to the method, the convergence and the stability of federal learning in a medical data processing task are improved, so that each medical institution can obtain a model with stronger generalization ability by using smaller computing resources and communication resources, and doctors can be assisted in analysis in various scenes such as disease diagnosis and risk assessment.

A medical training model includes a base frame, a fluid system arranged on the frame and configured to simulate a blood circulation replacement system, and at least one additively manufactured individualized vessel model with a patient-specific replicated lumen. The vessel model may be interchangeably connectable to the fluid system by at least one hydraulic quick coupling having coupling pieces such as a plug and a sleeve or bushing. Each coupling piece may contain an inner flow channel, and at least one coupling piece is designed as an adapter with a diameter-changing flow channel course that transitions from the patient-specific lumen to a standardized coupling connection opening. The flow channel course may include a conical region providing a smooth, edgeless transition between differing diameters, with the conical region formed as a hollow shaft truncated cone for flat bearing and sealing when the coupling pieces are locked together.

The invention provides a medical big language model training and reasoning method and device, equipment, a medium and a product, and relates to the technical field of natural language processing, and the method comprises the steps: constructing a medical training data set; training a medical big language model in combination with the medical training data set, and constructing target training data according to the high-quality medical data training set; and the target training data is substituted into the medical big language model for dynamic splitting training, so that an input original question is split to obtain a plurality of sub-questions, each sub-question is processed to obtain a sub-answer corresponding to the target sub-question, and a comprehensive and accurate reasoning result is formed. Different calling tools are adopted to carry out tool calling training on the medical large language model for processing sub-problems; by combining dynamic splitting training and tool calling training, the performance and practicability of the medical big language model are remarkably improved, and the efficiency and depth of the model for processing medical complex problems are improved.