80 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 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 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 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 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.

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 relates to a flight load-based multi-mode pilot physical and psychological state evaluation method and system, and belongs to the field of aviation medicine training. Comprising the steps that multi-mode physiological signals of pilots in a resting state are collected and preprocessed, resting physical and psychological state physiological indexes are extracted, and an individual resting baseline value of each pilot is calculated based on the resting physical and psychological state physiological indexes; wherein the multi-modal physiological signals comprise electrocardio signals, respiratory waves, myoelectricity signals and electroencephalogram signals; the method comprises the following steps: collecting and preprocessing multi-modal physiological signals of a pilot after flight, and extracting physical and mental state physiological indexes after flight; based on the physical and mental state physiological indexes after flight and the individual resting baseline value, the multi-dimensional recovery degree after flight of the pilot is calculated; generating a pilot physical and psychological state evaluation result based on the multi-dimensional recovery degree; wherein the multi-dimensional recovery degree comprises at least one of a brain nerve state recovery degree, an autonomic nerve state recovery degree, a psychological stress and stress recovery degree, a cardiovascular system recovery degree and a muscle state recovery degree.

The invention provides a hospital infection operation intelligent training system and method based on ST-GCN and AR fusion, and relates to the technical field of mobile medical training and artificial intelligence crossing. The system comprises a cloud management platform which uniformly governs SOP and AR scenes, performs score configuration, courses and schedules, completes data storage and ETL, performs intelligent analysis and report output, issues configuration to edges and AR, and is in butt joint with an in-hospital system; the multispectral visual acquisition subsystem is used for performing object environment recognition and pollution visualization based on synchronous calibration and a region of interest; the edge calculation unit executes preprocessing, space-time diagram construction and ST-GCN reasoning, and outputs an AR visualization instruction and a structured result; the user client supports billboard auditing, management configuration, appointment learning and process control. The system solves the problems of non-uniform standard subjectivity, low scene reduction degree, high organization cost, difficulty in hierarchical assessment of high-order capability and the like, and improves the coverage rate, fairness and clinical operation level.

The application provides a positioning data smoothing prediction method for multi-person interaction in a VR scene, a device and a medium. The method comprises: obtaining first action data of a target operator at a current time and a previous period, second action data of the remaining operators in the same virtual scene as the target operator at the current time and the previous period, and a medical training task in the virtual scene; determining a first behavior action and a first operation object of the target operator at the current time according to the medical training task and the first action data; determining an interactive operator who cooperates with the target operator according to the first behavior action, the first operation object, the first action data and the second action data; and predicting a predicted pose of the target operator at a next time based on the first behavior action, the first operation object, the first action data and the second action data of the interactive operator. The application can accurately predict the pose of the operator at the next time, and improve the continuity of the operator's action.

The application relates to the technical field of medical equipment, in particular to a training device for respiration and critical illness medicine, which comprises a saliva treatment module; an entertainment module for entertainment during children's respiration training is spirally connected to one side of the saliva treatment module; one end of a first hose is communicated with an airflow detection module for exhaust quality detection during children's respiration training; the other end of the airflow detection module is communicated with a second hose; the other end of the second hose is communicated with a sterilization module for gas sterilization; when respiration training is carried out on children patients, rotating the rotating ring on the outside can switch the second magnetic ring at different positions to cooperate with the rotating ring; different entertainment tools can be arranged above through the arrangement of a first hollow sleeve, a second hollow sleeve and a third hollow sleeve; the children blow the saliva treatment module, at this time, the entertainment toys above the entertainment module are started, which helps to attract the children to carry out multiple respiration training, helps to effectively exercise the lungs and is beneficial to subsequent rehabilitation work.

The invention relates to the technical field of medical training instruments, in particular to a neuroendoscope simulation training device which comprises a base, a display screen and a polishing rod, a plurality of air bags are arranged on the inner side of the base and used for extruding a skull model, and an air pressure sensing control mechanism is arranged between the base and the corresponding air bags and used for controlling the pressure of a trainee when the strength of the trainee reaches the pressure in the skull. According to the neuroendoscope simulation training device, the air bag on the inner side of the base is matched with the air pressure sensing control mechanism, when the operation force of a trainee reaches the preset intracranial pressure, the air bag can be extruded to promote a piston disc and a spline shaft to act, and the master control device triggers the alarm; meanwhile, the electromagnetic valve can adjust the air pressure of the pressure reduction cavity, simulate a real intracranial physiological pressure environment, help trainees to train operation skills within a safe pressure range, restore core scenes of pressure sensing and force adjusting in clinic, and avoid disjunction of training effects and clinical requirements.

A computer-implemented method, computer-system and computer-program product for generating medical training data for training artificial-intelligence (AI) algorithms for supporting clinical reporting and documentation are described. To generate the training data medical image data of a patient comprising medical image data elements are received and a medical findings report is generated, edited and / or received that summarizes individual medical findings. It comprises machine-readable findings-report elements the contents of which comprise semantic features. The contents of the findings report elements are automatically assigned to unique identifiers, wherein each identifier uniquely represents the medical semantic content of exactly one individual medical finding. The medical image data are annotated by linking one or more medical image data elements to the unique identifiers of one or more contents of the findings-report elements, and the annotated received medical image data are stored as training data for AI algorithms for supporting clinical reporting and documentation.

An artificial tissue includes: a first layer that is made of a conductive material and is provided on a front surface side of the artificial tissue, the first layer having a front surface portion and a side surface portion; a second layer that is made of a conductive material and is provided on a back surface side of the first layer, the second layer having a front surface and a side surface; and a support layer that supports the first layer and the second layer from a back surface side of the artificial tissue, in which the front surface portion of the first layer covers the front surface of the second layer, and the side surface portion of the first layer covers the side surface of the second layer.

A cricothyrotomy medical training device includes a ribbed tube representative of a human trachea, the ribbed tube having a first end and a second end. A first protrusion extends from the first end of the ribbed tube, the first protrusion being representative of a human thyroid cartilage. A second protrusion extends from the ribbed tube near the first end, the second protrusion being representative of a human cricoid cartilage. The second protrusion is longitudinally spaced between the first end and the second end. A first marking material is disposed at the first and second protrusions for representing a location of human skin. A second marking material disposed between the first and second protrusions to identify a location of a human cricothyroid membrane.

The invention relates to medical training aids. The claimed method includes preparing models of bone and cartilage structures and molds for blood vessel and thyroid models by 3D printing. A plastisol containing polyvinyl chloride is used for simulating a thyroid and blood vessels. This plastisol is poured into the molds for the blood vessel and thyroid models and held until it has cooled. A plastisol containing powdered graphite and a metal glitter for echogenicity is used for simulating lymph nodes and tumorous growths. Lengths of jute twine are laid in molds, into which a plastisol with added glitter is poured, the plastisol is held until it has cooled, and neck muscle models are cut out. A plastisol containing dyes is poured into a master neck mold to produce a skin model. The models of the muscles, vessels, thyroid, bone and cartilage structures, trachea, lymph nodes and tumorous growths are arranged in the master neck mold, into which a plastisol containing glitter is poured, the assembly is left to cool before being removed and the skin model is attached. The technical result is that the structure of the phantom more closely resembles the structure of a human thyroid.

This invention provides a medical training system, comprising: a standardized feature library construction module, which breaks down target medical operations into multiple standard steps and defines a standard value range for each step; a competency standard library construction module, which defines multiple competency levels and associates them with corresponding target medical operations; a case provision module, which provides simulated clinical cases according to competency levels; an error recording module, which determines that an operation error is triggered when the monitored operation feature data deviates from the standard value range, and generates a personal error file; and a targeted training module, which, in response to a selection command, retrieves the training scenario of the operation error node and generates a specific training task for the operation error. Therefore, this invention establishes an objective, quantitative, and standardized operation evaluation system, transforming subjective operation evaluation into objective measurement, enabling graded judgment of operation compliance, reducing the subjectivity and variability of human evaluation, and improving the effectiveness of nurse training.

This invention provides a method for online force assessment of characters in a virtual reality environment, aiming to achieve real-time quantitative assessment, physical accuracy, and high robustness of force on humanoid character movements in virtual reality environments. This method combines a motion generation model and a physics optimizer, using an end-to-end integrated approach to dynamically estimate key mechanical parameters such as joint torque and ground reaction force. Specific steps include: acquiring user motion trajectory data from sparse sensors (such as VR headsets and controllers); generating preliminary full-body motion postures using a motion generation model based on discrete latent space learning; performing physical correction and force estimation on the motion using a physics optimization module (employing a dual proportional-differential controller and a motion tracking optimizer); integrating motion data and force parameters; and outputting character animation and force visualization results in real time through a rendering engine. Through the collaborative design of motion generation and physical force assessment, this invention significantly improves the realism, real-time performance, and adaptability of virtual character movements, making it suitable for VR applications such as security monitoring, medical training, and sports simulation, enhancing immersion and interaction efficiency.

The invention relates to the technical field of virtual reality medical training, and discloses a cardio-pulmonary resuscitation simulation training system based on virtual reality. The system comprises a situation configuration module, an operation driving module, a physiological feedback module, an operation guidance evaluation module and a data integration and tracing module. The system receives a situation selected by a user and loads a corresponding anatomical model and pathological data to generate a virtual patient; collecting a user gesture and a force application vector through VR equipment, and driving the model to deform; deducing a vital sign sequence in real time by combining deformation and pathological data, and dynamically adjusting the physiological characterization and sound effect of the patient; performing frame-by-frame space-time alignment and difference measurement on the user operation and the standard regulation, rendering a guide mark at a corresponding position of the VR view field, and judging whether the operation generates expected physiological intervention or not based on the vital sign sequence; and various types of data are integrated to form a traceable training timeline. According to the system, high-simulation training of dynamic physiological response and immersive space guidance is realized.

The present disclosure relates to devices and methods for simulating active bleeding of wounds. The system can be used as a training aid for wound packing and tourniquet application. The system can be used in Tactical Combat Casualty Care (TCCC) to enhance medical training for military personnel. The device comprises a simulated appendage featuring distinct sections for wound packing and tourniquet application. Simulant blood is delivered through fluid channels, delivering blood to interchangeable wound profiles situated within the wound packing sections. This allows for varied training scenarios by repositioning the wound profiles across multiple sections of the appendage. Additionally, the tourniquet section includes channels enabling the cessation of blood flow upon correct application, providing clear visual feedback for successful training. The device effectively addresses key training goals: active bleeding simulation, visual feedback mechanisms, and variability in training scenarios, making it an invaluable tool for TCCC instruction.

The present application discloses and claims a kidney phantom consisting of a silicone rubber model featuring the anatomical shape of a human kidney, the renal artery, the renal vein, and a total of 11 tumors and 2 cysts. Each tumor is represented by a solid mass, while cysts are empty three-dimensional shapes. In order to accommodate the needs of both novice and advanced surgeons, tumors were designed with increasing surgical difficulty. The kidney phantom disclosed in the present application can be combined with a commercially available ensemble of laparoscopic tools to provide localized and remote training to surgeons and medical staff practicing laparoscopic and robotic partial nephrectomy.

An electronic system and method for providing interactive educational content and immersive learning experience are disclosed. According to one embodiment,?

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 utility model belongs to the technical field of rehabilitation medical treatment, and discloses a portable limb rehabilitation medical training robot which comprises a deck chair, the deck chair comprises a horizontal seat plate, a backup plate hinged to the front portion of the seat plate, a leg plate hinged to the rear portion of the seat plate, and a first belt pulley fixedly connected to the end of a rotating shaft, connected with the seat plate, of the backup plate. The upper limb rehabilitation device is simple and reasonable in structure, the backup plate is driven by the first motor, a patient can complete supine movement in cooperation with the traction structures, rehabilitation of the four limbs of the patient is facilitated, the coordination ability of the patient can be exercised, and the rehabilitation effect of the patient is improved. And the backup plate moves together with the patient, so that not only can the patient be assisted in moving, but also the patient can be prevented from being injured due to accidental falling down.

The application discloses a VR interaction method and system of multimodal tactile feedback and a storage medium, relates to the technical field of virtual reality, and comprises the following steps: step one, modeling of physical properties of virtual objects; step two, detection of interaction events and stratification of contact states; step three, generation of layered and progressive multimodal signals; step four, cross-modal collaborative compensation; and step five, output of tactile feedback signals: after smoothing processing is performed on the comprehensive tactile feedback signals, the signals are output to a multimodal tactile feedback device. The application 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, and users can truly perceive the material, temperature, weight and surface characteristics of virtual objects in these scenes, thereby significantly expanding the application range and practical value of VR technology.

Stump simulator for medical training purposes, especially for plaster casts or palpation exercises, characterized by an anatomical bone model enclosed by a soft layer of silicone gel or a similar soft material, wherein the external shape and texture of the soft tissue layer corresponds to a real amputation stump of a human extremity.

This invention provides a medical question-answering method integrating cross-modal hybrid experts. The method includes: acquiring medical images and question text, and preprocessing them to obtain initial visual embedding matrices and initial text embedding matrices; inputting the initial visual embedding matrices and initial text embedding matrices into a trained medical question-answering model to obtain answer text for the medical images and question text; the medical question-answering model includes a visual encoder, a text encoder, a positional encoding module, a feature fusion module, a gated router, a hybrid expert module, and an answer generator. It utilizes a cross-modal Transformer to achieve deep semantic alignment of image and text features, and dynamically schedules multiple expert networks through a gated router to perform specialized processing on different feature vectors, enhancing the model's adaptability to complex lesions and rare cases; it performs multi-angle reasoning to generate accurate and reliable answers, providing an efficient and reliable solution for assisted diagnosis, medical training, and scientific research analysis.