1680 results about "Medical systems" patented technology

In one embodiment, a medical system includes a catheter configured to be inserted into a chamber of a heart of a living subject, and including multiple electrodes configured to capture electrical activity from electrical activation signals propagating in tissue of the chamber, a display, and processing circuitry configured to automatically select bipolar signals to be captured into an electro-anatomical map from respective electrode pairs of the multiple electrodes responsively to an alignment of the respective electrode pairs with a direction of propagation of the electrical activation signals, and render the electro-anatomical map to the display.

The invention relates to the technical field of medical systems, and particularly discloses an anesthesia complication prediction model construction method based on deep learning, and the method comprises the following steps: S1, obtaining multi-source heterogeneous anesthesia medical data; s2, constructing a multi-modal feature fusion module; s3, designing a hierarchical deep neural network architecture which comprises sub-networks for processing different modal data in parallel and a full-connection prediction layer fusing multi-modal features; s4, continuously outputting a complication probability curve in a sliding time window mode by adopting a dynamic risk trajectory prediction mechanism instead of a single static prediction result; and S5, deploying a clinical real-time decision interface, and mapping a prediction result to an anesthesia monitoring equipment alarm system in real time. A bidirectional LSTM + 1D-CNN hybrid encoder and a cross-modal attention mechanism are adopted, time sequence dependence of physiological signals and spatio-temporal characteristics of operation events are synchronously captured, deep semantic fusion of multi-source data is achieved, and the characterization capacity of a model for precursor characteristics of complications is improved.

A fluid management system for use in a tissue resection procedure includes a controller. An inflow pump is operated by the controller and configured to provide fluid inflow through a flow path to a site in patient's body. An outflow pump is operated by the controller and configured to provide fluid outflow through a flow path from the site in patient's body. A motor driven resecting device may be provided for resecting tissue at the site. The controller is configured to actuate an inflow pump and an outflow pump in response to various signals and various algorithms are provided to provide malfunction warnings and assure safe operation.

The invention relates to the technical field of intelligent medical systems, and discloses a cerebral apoplexy onset risk assessment and reminding method and system.The method comprises the steps that continuous medical structured detection data are collected, and the data comprise carotid artery blood flow parameters, brain oxygen saturation, heart rate variability and metabolic indexes; inputting a bidirectional LSTM, a differential convolutional network, a wavelet residual network and a multi-layer perceptron to extract nonlinear features; constructing a neural function coupling structure diagram of four nodes of cerebral blood supply, oxygen supply, autonomous regulation and metabolic steady state; calculating inter-node time sequence offset correlation and a stable factor to obtain a coupling anomaly coefficient; and driving the embedded network by using a graph structure and a node feature input mechanism, and outputting a risk state assessment result. According to the method, the neural function coupling structure diagram is constructed and the mechanism is introduced to drive the embedded network, so that high-precision identification of the multi-system collaborative abnormal state and dynamic evaluation of the stroke risk level are realized.

The invention discloses a medical knowledge constrained multi-modal time series data dynamic evaluation method and a wearable medical system. According to the method, a mixed perception architecture is constructed through an improved Swin-Transform time sequence encoder and a graph neural network, a medical priori mask matrix is embedded to constrain an attention mechanism, and time sequence feature extraction and cross-parameter correlation modeling of multi-modal physiological signals are enhanced; a dynamic quality evaluation model is designed, data integrity, cross-modal consistency and diagnosis effectiveness indexes are fused, an evaluation weight is adjusted in real time in combination with an adaptive weight function, and quantitative association of data quality and clinical diagnosis confidence is achieved; and when the motion artifacts or pathological conflicts are judged to exist, starting an abnormal positioning and correcting mechanism. According to the method, noise such as motion artifacts and interference can be monitored at any time, the attention to different indexes is adjusted in real time according to the risk level, and the pathological recognition accuracy and clinical credibility are improved by fusing multi-mode perception of medical knowledge.

The invention discloses a multidisciplinary combined diagnosis and treatment method and system, relates to the technical field of intelligent medical systems, and solves the problems that traditional manual consultation is low in efficiency and is greatly interfered by human factors, simple system recommendation is inaccurate, and diagnosis and treatment requirements of patients are difficult to meet. The method comprises the steps of generating a consultation scheme based on a preset matching degree standard, an illness condition and a diagnosis and treatment specification, triggering a payment process after system compliance verification, and locking consultation resources after payment succeeds; according to the illness state emergency degree of the patient, expert scheduling information and hospital resource occupation condition, intelligent scheduling is carried out to generate a consultation time arrangement, and meanwhile, consultation invitation and detailed information are sent to related experts; and after the consultation is finished, the system arranges text records, fuses expert opinions, generates a structured consultation report and pushes the structured consultation report to a patient end. The method has the advantages that intelligent management of multidisciplinary joint diagnosis and treatment is achieved, medical resource configuration is optimized, diagnosis and treatment efficiency and accuracy are improved, and medical experience of patients is improved.

The invention discloses a mild cognitive impairment brain rehabilitation training device and system in the technical field of cognitive nerve rehabilitation, and the device comprises a multi-modal data fusion module, a personalized training task generation module, a multi-sensory stimulation presentation module and a real-time adaptive regulation and control module. And the personalized training task generation module is used for dynamically generating a personalized narrative training task fusing the user interest theme and the cognitive training target by utilizing a reinforcement learning algorithm based on the user cognitive weak domain evaluation result and the user personal interest and life narrative content obtained through natural language processing. Clinical data of a patient and real-time electroencephalogram and behavior data are fused, a narrative training task deeply fused with life experience and interest of the patient is dynamically generated, the difficulty of the training task is automatically adjusted according to real-time physiological indexes, meanwhile, seamless connection with a medical system is achieved through digital biomarkers, and the training efficiency is improved. And the precision and compliance of rehabilitation training are obviously improved.

The invention discloses a medical dialogue search term rewriting method and device, equipment and a storage medium. The method comprises the following steps: detecting whether current dialogue input is a question or has intention jump through a lightweight model for multi-round dialogue intention jump and question recognition; when it is detected that the current dialogue input is subjected to intention hopping or not questioning, the historical dialogue context is emptied, and the current input serves as independent query to be directly output; and when it is detected that the current dialogue input does not have intention hopping and the current dialogue input is a question, triggering the functions of substitution disambiguation and semantic completion, calling the fine-tuning and rewriting large model, and generating a standard medical question with complete semantics and standard terms, so that the context dependency relationship in the user dialogue can be accurately identified, and the user experience is improved. In addition, a large model can be called to complete deep semantic reconstruction when necessary, light model pre-judgment and large model accurate rewriting are combined, accuracy and efficiency are both considered, and the method is a key technical breakthrough for improving usability and specialty of a medical RAG system.

The invention relates to the technical field related to medical systems, in particular to a medical teaching and research integrated system based on a common bottom layer, and aims to break through information islands among medical treatment, teaching and scientific research and realize closed-loop linkage and dynamic knowledge conversion of a whole process. The system comprises a diagnosis and treatment decision subsystem, a scientific research queue management subsystem and a teaching subsystem, integrates data through a three-in-one data engine, and realizes information sharing and cooperative work. The diagnosis and treatment decision subsystem provides diagnosis and treatment support for severe diseases based on an expert thinking chain. And the scientific research queue management subsystem is responsible for screening cases, sorting data, implementing quality control and statistical analysis, and adjusting an expert thinking chain according to scientific research results. And the teaching subsystem carries out teaching by using a standardized case presentation mode. Through a feedback closed loop of clinical discovery, scientific research verification, knowledge updating and teaching feedback, rapid propagation and application of medical knowledge are promoted, and the medical service quality and the medical education level are improved.

The invention relates to a layout image generation method and device, computer equipment and a storage medium, and the method comprises the following steps: carrying out the element analysis of original image data, and obtaining image element information; performing feature extraction on the image element information to obtain image features and text features; performing hypergraph structure construction according to a preset design rule, the image features, the text features and the image element information to obtain hypergraph structure information; performing style parameter adjustment and coordinate adjustment on the hypergraph structure information according to the matching similarity corresponding to the element embedding vector to obtain to-be-rendered information; and performing image layout rendering according to the to-be-rendered information to obtain an effective layout image. The method can be applied to application scenes of financial science and technology and digital medical systems, and insurance product propaganda images or disease prevention propaganda images which effectively express image elements and features can be effectively, accurately and quickly generated according to original insurance product images or health education images in the system.

Medical systems, devices, and methods for determining whether a blood vessel is a vein or an artery. The system includes an optical fiber configured for insertion into a blood vessel coupled with a console having a light source, an optical receiver, processors, and logic stored in memory. The optical fiber includes sensors disposed along its length configured to determine a state or condition of the optical fiber. The state or condition can include a strain, movement, pressure, and / or temperature. The logic is configured to analyze reflected signals from the sensors to determine whether the optical fiber is inserted within an artery or within a vein. The logic may also determine a red-blue shift of a projected light to determine a blood flow direction with respect to the optical fiber.

Systems or techniques that facilitate automated role-based access control for patient health information security and compliance are provided. In various embodiments, a system can access historical data corresponding to medical systems. In various aspects, the system can generate, based on the historical data, automated role-based access control rules. In various instances, the system can employ a trained machine learning model to detect, based on the automated RBAC rules, user access anomalies from audit logs of the medical systems. In various embodiments, the system can visually render the detected user access anomalies on a graphical user interface.

Disclosed herein is a medical system (100, 400) comprising: a magnetic resonance imaging system (102) configured for providing a respiratory signal. The respiratory signal is descriptive of a respiratory position as well as of inhalation and exhalation of the subject. The execution of machine executable instructions (140) causes the computational system to repeatedly: perform (200) the individual acquisition of k-space data using the magnetic resonance imaging system; receive (202) the respiratory signal; bin (204) the individually acquired k-space data into one of a predetermined number of k-space bins (148) using the respiratory signal and perform outlier rejection. Execution of the machine executable instructions further causes the computational system to reconstruct (208) a magnetic resonance image for each of the predetermined number of k-space bins to provide a respiratory-state resolved magnetic resonance image (150).

The invention belongs to the technical field of complication prevention, and particularly relates to a rehabilitation equipment integrated management and data interaction system based on a mobile terminal, which comprises a multi-source data acquisition module, a data integration and preprocessing module, an intelligent analysis module and a risk early warning module, data islands are cracked through a multi-source heterogeneous data fusion architecture, and an extensible access layer is designed; the system is compatible with medical systems such as HIS, LIS and PACS; a Bluetooth module, a Wi-Fi module and a 5G module are embedded and directly connected with rehabilitation equipment such as a power bicycle and an inspiratory muscle training instrument; cross-institution data collaboration is realized through a federated learning framework, for example, local training sub-models of hospitals and global parameter aggregation of a central server are realized, model generalization is improved on the premise of privacy protection, 'preoperative, intra-operative and post-operative link splitting 'is broken, and a perioperative period full-link data set is constructed.

The invention provides a trauma knowledge graph construction method and system based on a three-level fault-tolerant mechanism. The method comprises the following steps: acquiring a trauma condition core knowledge framework; extracting trauma condition rules based on the trauma condition core knowledge framework, and establishing a trauma condition rule base; dynamically generating a target cue word based on the trauma condition rule base by utilizing a large language model; processing the target text based on the target cue word by using a large language model to generate a candidate file; verifying the candidate file to obtain a verification file; and constructing the trauma knowledge graph based on the verification file. According to the scheme, a mode of combining ontology construction from bottom to top and entity node construction from top to bottom is adopted, various methods such as manual construction, rule extraction and a large language model are fused, the knowledge extraction precision and the map coverage degree are improved, the information processing capacity and the treatment decision-making efficiency in a complex trauma condition scene are remarkably improved, and the method is suitable for popularization and application. And construction of an intelligent medical system is supported.

A computer-assisted medical system includes a user device wearable by an operator. The user device includes a display device configured to present imagery overlaid in an environment of a manipulator assembly, and a sensor configured to detect one or more landmarks in the environment. The medical system includes a controller configured to execute instructions to perform operations The operations include receiving, from the sensor, position or orientation information for the one or more landmarks in the environment, and directing a manual movement of a portion of the manipulator assembly by causing the display device to present the imagery overlaid in the environment based on the received position or orientation information.

The embodiment of the invention provides a medical system working method and device based on a large model and a knowledge graph. The method is applied to information medical field. The method comprises the following steps: searching contents related to patient chief complaint information from a constructed medical knowledge graph by utilizing a large model, analyzing the patient chief complaint information based on the searched related contents, identifying a patient disease, and giving a corresponding doctor-seeing department; the distance between the hospital and the patient position is smaller than a preset range, and the hospital comprises a doctor-seeing department, and number source data of the target hospital are obtained in real time; according to the number source data and the evaluation factors of the doctors in the doctor-seeing departments in the hospitals, a doctor recommendation result is generated through a large model, and constraint examination is conducted on the doctor recommendation result; and after the constraint examination is qualified, performing multiple rounds of inquiry on the patient according to the patient chief complaint information by utilizing the large model, generating a pre-inquiry report and sending the pre-inquiry report to the patient or a doctor in a recommendation result. Therefore, the doctor recommendation accuracy and the doctor seeing efficiency are improved, and the doctor seeing pressure of a hospital is relieved.

The invention relates to the technical field of on-line monitoring, in particular to an insulation fault on-line monitoring method and system for a medical IT system control cabinet. The method comprises the following steps: selecting a non-integer frequency multiplication detection frequency according to equivalent ground distributed capacitance in a previous monitoring period, and superposing the non-integer frequency multiplication detection frequency with a fundamental frequency to form a composite signal injection system; collecting signals to calculate a voltage instantaneous harmonic distortion rate change gradient, and selecting a time window to split the leakage current according to the change gradient; on the basis of dispersion estimation signal-to-noise ratio weighted fusion current vectors, insulation resistance and distributed capacitance are calculated; constructing a four-dimensional original feature vector composed of the insulation parameter, the phase difference and the intermodulation component energy; constructing a diagonal weighting matrix based on the resistance-capacitance ratio and the gradient to transform a feature vector, and determining the fault attribution through the Mahalanobis distance calculation with the fault template library. According to the scheme, harmonic interference can be avoided, calculation stability can be improved, insulation instantaneous sudden change can be quickly responded, and different fault types can be accurately distinguished to guide maintenance.

A medical system comprises a display system, an input system including a first control device, and a control system. The control system includes a processing unit configured to display an image of a field of view of a surgical environment, determine a first keypoint on a first tool in the surgical environment, determine a selection region associated with the first keypoint of the first tool, determine a position of a cursor relative to the field of view that corresponds to a position of the first control device, and determine if the position of the cursor overlaps the selection region. The processing unit may also be configured to provide a directional cue if the position of the cursor overlaps the selection region to direct the cursor toward the first keypoint and engage the first control device with the first tool when the cursor reaches the first keypoint.

The invention, which relates to the field of diabetes nursing, discloses a multi-model fusion personalized diet recommendation system for diabetes mellitus, comprising a recipe recommendation decision module based on a system architecture, a blood sugar prediction module, and an intelligent question and answer interaction module. The nutritional requirements and the blood sugar control target of the patient are met at the same time; the blood glucose prediction module predicts the postprandial blood glucose level according to the current blood glucose level of the user and the recommended recipe; the intelligent question and answer interaction module provides convenient natural language interaction, collects user feedback and dynamically adjusts recommended content. Compared with the prior art, the method has the advantages that scientific diet recommendation is provided, the recommendation result is optimized through a user feedback mechanism, and the recommendation accuracy and practicability are ensured; a convenient health management tool is provided, and the compliance of the patient on diet management is enhanced; the health condition of the diabetic patient can be improved, the burden of a medical system is relieved, and the public health level is improved.

The invention relates to the technical field of data classification, in particular to a hospital intelligent inquiry log analysis method and system.The method comprises the following steps that medical concept depth is counted, symptom keywords are extracted, statistical information gain and semantic consistency scores are calculated, split decision-making indexes are generated by combining the gain and the scores to construct an intention classification tree, and an intention classification result is obtained; inputting a log to generate an inquiry intention identifier, recognizing an error node based on diagnosis and treatment correction feedback and updating a punishment value, establishing a virtual error correction link, calculating a path confidence fusion node, and establishing a physical branch to generate a reconstruction classification tree when a guide weight exceeds a limit. According to the method, keyword statistics is upgraded into three-dimensional feature analysis by introducing medical concept hierarchies, a classification tree following a medical system is constructed, a reverse penalty and virtual link mechanism is constructed by using feedback, node fusion and reconstruction are executed based on confidence, classification logic self-adaptive evolution is realized, and intention recognition interpretation and robustness are improved.

The present disclosure provides a patient-side operation device, a surgical robot, and a surgical medical system. The patient-side operation device comprises a base, a position adjustment mechanism, a posture adjustment mechanism, and an end effector holding mechanism that are sequentially connected. The position adjustment mechanism is configured for moving the posture adjustment mechanism and the end effector holding mechanism. The end effector holding mechanism is configured for mounting a surgical end effector and is provided with a pitching joint rotatable around a pitching axis, and the pitching axis is perpendicular to the extending direction of the surgical end effector. The posture adjustment mechanism is provided with a first pivotable joint rotating around a first pivoting axis and a second pivotable joint rotating around a second pivoting axis, wherein the first pivoting axis is at an angle to the second pivoting axis, and at least one of the first pivoting axis and the second pivoting axis is at an angle to the pitching axis.

The invention discloses an infection risk grading early warning method and system based on clinical parameter dynamic fusion. The method comprises the following steps: acquiring patient data from a plurality of medical systems to obtain initial data; performing regularization preprocessing on the initial data to obtain a preprocessing result; quantifying the importance of the preprocessing result in combination with clinical expert opinions and a machine learning model, and generating a final risk assessment weight through dynamic weighted fusion; based on the final risk assessment weight and the knowledge graph, factors influencing the risk and weights of the factors are determined, and a dynamic assessment model used for calculating a risk score is established; and performing continuous monitoring by using the dynamic evaluation model, and automatically triggering an alarm when the risk score exceeds a threshold value. By implementing the method provided by the invention, the accuracy and reliability of infection judgment can be effectively improved, false alarms and missing alarms are reduced, and more timely and accurate infection risk early warning is realized.

The present disclosure provides a method and system for overlay presentation of a skeletal image based on augmented reality, relating to the technical field of smart medical systems. The method includes obtaining fracture imaging data for a fracture region of a patient, the fracture imaging data including a CT image, an X-ray image, and a light-field image; parsing a ray model corresponding to the light-field image, the ray model providing spatial propagation information of a light in the fracture region of the patient; determining a multimodal fusion feature corresponding to the fracture imaging data based on the ray model and a feature fusion network; reconstructing a three-dimensional model of a fracture part based on the multimodal fusion feature; and aligning and calibrating the reconstructed three-dimensional model of the fracture part with the fracture region of the patient in an actual surgical scene.

The present invention provides a multilingual, AI-powered healthcare system integrating a personalized medical assistant, real-time clinical decision support, and closed-loop device control. The system ingests multimodal patient-generated and institutional data, applies advanced language-model-driven reasoning for clinical insights and triage, and supports regulated, auditable actuation of medical devices. Key features include multilingual overlays, role- and jurisdiction-specific visualization, co-signature enforcement, audit traceability, and seamless integration with healthcare infrastructure. A “Multilingual Overlay” is a dynamically generated display layer that the system composites in real-time from clinical text, icons, and color-coded indicators, automatically adapting its language, reading direction, terminology, visual density, and role-based data visibility to the preferences, locale, and device form-factor of each authenticated viewer. The architecture supports provider- and patient-facing use cases, agentic AI for autonomous yet regulated decision-making, and robust safety and compliance features.

The embodiment of the invention discloses an electrical stimulation parameter determination method, device and equipment, a medium and a medical system. The method comprises the following steps: acquiring a brain three-dimensional model corresponding to a target user of which the brain is implanted with at least one stimulation electrode; determining at least one electrode contact combination corresponding to the target nucleus simulation model according to the first spatial position information of the target nucleus simulation model and the second spatial position information of the electrode simulation model; determining at least one group of candidate electrical stimulation parameters of the electrode contact combination according to the electrode contact combination and a target nucleus simulation model; determining the total power consumption of the stimulator of the candidate electrical stimulation parameters according to a preset total power consumption quantitative model of the stimulator and the candidate electrical stimulation parameters; and determining at least one group of target recommended electrical stimulation parameters from the plurality of groups of candidate electrical stimulation parameters according to the total power consumption of the plurality of stimulators. According to the technical scheme, the effect of accurately screening the recommended electrical stimulation parameters with low energy consumption and efficient stimulation effect according to the electrical stimulation power consumption is achieved.

The invention relates to a recommendation output method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring user input information; performing intention analysis on the user input information to obtain user intention information; generating an initial recommendation candidate set according to the user intention information and a preset knowledge base; obtaining dialogue state information, and dynamically adjusting the initial recommendation candidate set according to the dialogue state information based on a pre-trained reinforcement learning model to obtain an optimized recommendation candidate set; dialogue progress information is obtained, the optimized recommendation candidate set is converted into language description information, and recommendation result information is generated according to the language description information and the dialogue progress information; and performing recommendation output according to the recommendation result information. The message pushing method and device can be applied to application scenes of a financial service system and a digital medical system, and accurate message pushing can be effectively achieved according to the information input by the user.

The invention relates to a traditional Chinese medicine syndrome intelligent identification method and system based on multi-modal data fusion, and belongs to the technical field of medical systems, and the method comprises the following steps: S1, multi-modal traditional Chinese medicine data acquisition: obtaining at least two different types of original data related to traditional Chinese medicine; s2, performing cleaning, denoising, normalization and data alignment processing on the various types of original data acquired in the step S1 to obtain standardized multi-modal data; s3, multi-modal data fusion is carried out, and a feature layer fusion or decision-making layer fusion mode is adopted; s4, identification model training: matching the fusion feature vector with a corresponding traditional Chinese medicine syndrome label, and inputting the matching result into a preset deep learning model for training to obtain a traditional Chinese medicine syndrome identification model; and S5, intelligently identifying the traditional Chinese medicine syndromes, and outputting a corresponding traditional Chinese medicine syndrome identification result. According to the method, full-process automatic processing and closed-loop optimization are realized, and the dependence on the professional level of operators is greatly reduced.

A medical system useful in the determination of future disease progression in a subject. More specifically the present invention applies machine learning techniques to aid prediction of disease pathology and clinical outcomes in subjects presenting with symptoms of cognitive decline and to expedite clinical development of novel therapeutics.

A computer-assisted medical system includes a user device wearable by an operator. The user device includes a display device configured to present imagery overlaid in an environment of a manipulator assembly, and a sensor configured to detect one or more landmarks in the environment. The medical system includes a controller configured to execute instructions to perform operations The operations include receiving, from the sensor, position or orientation information for the one or more landmarks in the environment, and directing a manual movement of a portion of the manipulator assembly by causing the display device to present the imagery overlaid in the environment based on the received position or orientation information.