123 results about "Clinical settings" patented technology

The invention discloses a brain MRI image segmentation method based on an RWKV model, and belongs to the technical field of medical image processing and artificial intelligence crossing. Firstly, an RWKV linear self-attention module is introduced into a U-Net network framework, and association between remote pixels is established with relatively low calculation overhead, so that the recognition precision of a brain tumor area is improved, the reasoning time is effectively controlled, and the practicability of a model is enhanced. Secondly, according to the method, multi-scale coding and a feature fusion mechanism are combined, local and global information is extracted in a combined manner, features are mined from different resolution levels, and adaptive fusion is realized, so that the fine-grained segmentation effect is improved. And finally, in order to enhance the generalization ability and lightweight deployment performance of the model, the network structure is further optimized, and the overall computing resource demand is reduced, so that the method can better adapt to cross-patient MRI data in a complex clinical environment, and has good practical value and popularization potential.

The present invention relates to a communication system and method designed for patients in clinical settings who face challenges using standard communication devices. This system includes a mobile application and associated devices that establish a secure, asynchronous communication channel between clinicians, patients, and their support networks. Authorized users can record and send voice messages and other media, which are automatically played on the patient's device without requiring active operation. The system is tailored for ease of use, accommodating patients with severe physical or cognitive limitations through hands-free operation and simple commands. It adapts to the patient's condition, pausing messages during rest or medical procedures and resuming when appropriate. The system also features cross-linguistic messaging, translating and synthesizing voice messages across languages while maintaining the speaker's voice. Additional features include support for rich media, automatic transcription, and AI-driven content analysis. Secure, encrypted channels ensure privacy, complying with healthcare regulations. This invention enhances emotional connection and support for isolated patients, acting as a virtual visitation tool.

According to the clinical nursing patient information monitoring and inputting method and system, the data accuracy, the data inputting efficiency and the privacy protection safety are improved; the method comprises the following steps: firstly, collecting and preprocessing vital sign data of a patient in real time through various physiological sensors; then, acquiring a nursing record image, acquiring nursing record data through an OCR model, acquiring nursing information voice, transwriting the nursing record data and the nursing information voice into nursing information data through a medical scene voice transwriting model, and performing unified standardization processing on the three groups of data to generate patient information data; and finally, when patient information data is input, symmetric encryption is carried out by adopting a national cryptographic SM4 algorithm, encrypted data is obtained, a dynamic encryption key is generated through an ECDH key distribution technology, and the dynamic encryption key can be called to carry out decryption operation after authentication of a dual protection mechanism of Hash value pre-verification and identity authentication is passed.

The invention provides an imaging department doctor training method and system based on generative artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: calling a predetermined case parameterization strategy to carry out the parameterization processing of a real-time case, and obtaining a real-time parameter feature; extracting a first case in the image database, and obtaining a first parameter feature of the first case; generating a virtual case in combination with the real-time parameter features and the first parameter features, and introducing medical knowledge graph analysis to obtain a case reasonable coefficient of the virtual case; if the case reasonable coefficient is in a preset coefficient threshold value, constructing a virtual clinical environment of the virtual case; in a virtual clinical environment, image training is carried out on a target doctor in combination with a virtual case, and the problems that an existing image department doctor training system depends on fixed textbooks and standardized training cases and lacks a dynamic adjustment mechanism based on a real-time case, so that dynamic deduction and immersive interactive learning of doctors are difficult to realize, and the training efficiency is poor are solved. And the technical problem that the image diagnosis skill is difficult to improve efficiently is solved.

A health monitoring system is provided including at least one non-invasive wearable device capable of collecting and storing data, and external monitoring devices that displays and analyses the data for accurate monitoring and anomaly detection. The wearable devices are configured to collect low-latency PPG-derived bio-signals and can utilize multiple devices for accuracy and continuity. The system may further include a dashboard that analyzes the information as well as displaying basic information such as number of beds (in-use, available), staff-to-patient ratio, etc. The data can be collected and accessed remotely and may be utilized before, during, and / or after a patient is dispatched from a clinical setting.

The device of the present invention is an ultrasound device which is used in a clinical setting to help users locate the correct position and angle for needle insertion. The device provides images of perpendicular image planes without needing to move the device thereby allowing a user to maintain a steady view of the target and surrounding anatomy in multiple image planes as the needle is advanced. The device may comprise a needle guide assembly which can adjust the needle guide angle to control needle insertion. The device may comprise a boot which provides acoustic coupling, sterility and needle guidance.

The invention provides a deployment method, device and system for a specific detection model in a medical scene, is used for carrying out localized deployment on a mature medical examination model in a hospital, specifically combines a container deployment technology and a container arrangement technology, provides corresponding model calling services for medical personnel more conveniently, efficiently and safely, and improves the medical examination efficiency. Dual requirements of data security compliance and model real-time calling can be effectively balanced, a safe, extensible and high-reliability operation platform is provided for actual landing of a digital algorithm, external dependence is greatly reduced, and the real-time performance of the digital algorithm is improved. The method provides possibility for rapid verification and iterative algorithm of hospital informatization departments and researchers in a clinical environment, achieves intelligent medical assistance of closed-loop, safe, controllable and efficient optimization in a hospital, has large-scale popularization value, and provides robust AI technical support for various clinical diagnosis, inspection analysis and scientific research work.

A self-removal device for intrauterine device (IUD) extraction is described that includes a housing having a proximal handle and a distal aperture, and a grasper mechanism with stationary and movable elements for engaging and securing IUD threads, wherein the grasper is actuated by a lever positioned at the housing. Systems are also described comprising the device and ancillary components such as locking elements, grips, linkage assemblies, as well as kit embodiments that may include visualization aids, light sources, forceps, measuring tools, and instructions for use. A patient using the device, with or without assistance, can independently or semi-independently remove an IUD using the device and system in non-clinical settings, optionally with remote digital guidance or confirmation.

The invention provides a clinical traditional Chinese medicine knowledge graph construction method based on a multi-modal graph attention network. The method comprises the following steps: acquiring multi-modal data in a traditional Chinese medicine clinical environment; generating a corresponding type label; all modal vectors in the multi-modal semantic representation set are corresponding to nodes, and an initial traditional Chinese medicine clinical knowledge graph is constructed; based on attention enhancement of a graph neural mechanism, generating a graph after structure enhancement; obtaining a standard role set of the traditional Chinese medicine knowledge base, performing semantic role recognition, and generating a node set with role tags; calculating an edge importance score according to the normalized map, and screening out a final edge structure based on the edge importance score to generate a standardized map structure; and importing the standardized map structure into a map database to realize clinical knowledge calling. According to the method, a complete closed-loop process from original clinical data to deployment output is realized through multi-modal modeling, structure generation, graph optimization, role modeling and semantic feedback self-evolution.

A system and method for monitoring a patient are disclosed. The method includes receiving and pre-processing image data captured by a set of cameras positioned to acquire images of either medical instrument or a patient within a clinical environment. The method further includes extracting an instrument identification code from a tamper-proof marking in the image data and validating the code against a pre-registered instrument ID. The method also extracts instrument display data using OCR or a trained machine learning model to identify medical parameters displayed on the instrument. The extracted data is transmitted to a time series database on a room integrator computer or cloud storage for storage and further use. Additionally, the method analyzes facial images of the patient to derive medically relevant data, including facial expressions, skin colour corrected for illumination variations, and facial surface or volume changes based on 3-D depth image data. The time series database stores the analyzed medically relevant data and the instrument display data. A visualization and analysis interface is utilized to assist in clinical decision-making based on the stored data.

The invention provides a method and equipment for analyzing adverse event information of a clinical test. The method comprises the following steps: acquiring audio information of a target object; outputting a corresponding pinyin sequence based on the audio information by using a pre-trained speech recognition model; performing text prediction on the pinyin sequence to obtain an output text; in response to the fact that the knowledge-enhanced large language model is utilized to determine that an adverse event exists in the output text, key information in the output text is extracted; performing named entity identification based on the key information to obtain name information, time information and degree information in the key information; and constructing adverse event information based on the name information, the time information and the degree information. By implementing the method and the device, the analysis processing can be automatically performed according to the description of the target object to obtain the corresponding adverse event information, compared with manual processing, the speed is higher, the method and the device are not interfered by clinical environments or other factors, and analysis tasks can be efficiently completed in batches.

The invention relates to the field of medical imaging and clinical data machine learning, and discloses an ICAS stroke risk prediction method and system, and the method comprises the steps: carrying out the preprocessing of image data; clinical data such as past medical history, clinical symptoms and family history of the patient are collected; analyzing related biomarkers of the blood sample to obtain biomarker data, and selecting variables closely related to the stroke risk; then carrying out feature splicing to form a multi-modal fusion feature vector; and finally, training, verifying and testing by adopting a machine learning algorithm, and constructing to obtain an ICAS stroke risk prediction model. And based on the ICAS stroke risk prediction model, generating a stroke risk score for each patient, and generating a clinical decision support strategy. The performance of the stroke risk prediction method and system can be improved continuously, accurate risk prediction can be provided in different clinical environments, and accurate stroke risk assessment and targeted treatment schemes can be provided for ICAS patients.

The invention discloses a physiologically-driven standardized intelligent simulation patient system, which comprises an adult simulation patient, a computer, a computer, a computer, a computer, a computer, a computer, a computer and a computer, wherein the adult simulation patient is made of a simulation man-made high polymer material, is environment-friendly and pollution-free, has obvious anatomical marks, touches two nipples, ribs, sternum and xiphoid processes, is flexible in joints, and realizes various operation body positions; wherein the physiological data comprises the heart rate, the blood pressure and the respiratory rate, the data is combined with a built-in physiological model to simulate the physiological state of a patient in real time, a monitor is simulated, real-time physiological data monitoring and feedback are provided, and the vital signs, the heart rate, the blood pressure and the oxyhemoglobin saturation of the simulated patient are displayed; the system has strong physiologically-driven standardized intelligent comprehensive simulation patient, can perform situational teaching according to a real clinical environment, effectively assesses the clinical thinking and personal operation skills of students, can greatly improve the team cooperation spirit of the students, and simulates the real physiological response and clinical scene.

The invention provides a closed-loop reinforcement learning diagnosis method and system based on a high-fidelity virtual clinical environment, and the method comprises the steps: 1, carrying out the structural processing of basic information and an examination record set of a patient, and obtaining a standardized examination and diagnosis data set; 2, training to obtain a high-fidelity clinical environment system; and step 3, based on the high-fidelity clinical environment system, obtaining a diagnosis agent through reinforcement learning training. A simulation examination result highly consistent with the state of a patient can be generated based on real electronic health record data, a diagnosis agent performs multiple rounds of interaction in a virtual environment, autonomously explores a diagnosis path, and performs strategy optimization through a double-reward mechanism, so that the crossing of a diagnosis model from static prediction to dynamic decision is realized, and the diagnosis efficiency is improved. And the diagnosis accuracy and the reasonability of examination recommendation are greatly improved.

The present disclosure provides a healthcare platform that delivers monitoring services to patients in clinical settings and non-clinical settings. An example method for monitoring physiological data is described. The method comprises (a) contacting a surface of a subject with a patch, where the patch is configured to (1) monitor, with aid of one or more sensors operably coupled to the patch, physiological data from the subject, and (2) receive, at an electronic module in communication with the one or more sensors, the monitored physiological data; and (b) wirelessly transmitting the received physiological data to two or more different types of devices.

Clinical-quality eye examinations are provided at locations remote from the physician and outside of a clinical setting. A portable optical device and corresponding software are delivered to the patient's location. The portable optical device is configured for patient use in self-conducting an eye or vision exam and taking optical measurements. As a function of type of optical measurement data or health consideration of the patient, the corresponding software provides uploading and live streaming of the optical measurement data to the physician. The corresponding software is executed during the patient conducting the eye (vision) exam and enables the remotely located physician, to participate in the same via video conference. Alternatively, an artificial intelligence resource operating remotely or locally can provide physician-type guidance and eye health assessments. Alignment of device and patient eyes can be facilitated for remote exams.

Disclosed herein are systems, methods, devices, and media for carrying out detection of ophthalmic and systemic diseases and disorders. Deep learning algorithms enable the automated analysis of ophthalmic images such as retinal scans to generate accurate detection of various diseases and disorders. Point-of-care implementations allow for rapid and efficient detection outside of the clinical setting.

This disclosure is directed toward systems and methods for monitoring patients in a clinical setting by using non-contact multimodal imaging. For example, based on receiving imaging data, a computing device may identify an object and a region of focus formed by the object. The computing device may then cause a sensor to capture first sensor data at a first time and second sensor data at a second time, wherein the sensor data is associated with the region of focus. Based on determining that a difference between the first sensor data and the second sensor data is greater than or equal to a threshold difference, the computing device may generate and send, to an additional computing device, an alert indicative of the second sensor data.

Disclosed herein are methods for selecting and preparing patient data to facilitate the adoption and customized training of machine-learning models in clinical settings, particularly for radiation therapy treatment planning. The disclosed embodiments streamline the customized training process through an automated workflow that includes prefiltering patient metadata, retrieving relevant DICOM files, optional data anonymization, and generation of training data. The data is then organized into a format suitable for machine-learning training. The embodiments discussed herein reduce manual labor, minimize errors, and accelerate the integration of machine-learning into clinical workflows, enabling clinics to train and implement predictive models that replicate specific clinical practices, thereby enhancing treatment precision and improving patient outcomes.

The present invention relates to an apparatus for accurately positioning a transcranial magnetic stimulation (TMS) coil over a target position on a patient's head. The apparatus includes a TMS coil with an integrated real-time display device that shows the position of the coil relative to the patient's head. A processor for calculating the coils spatial coordinates using a three-dimensional tracking method and transmitting the information to the display. A synchronization mechanism providing real-time updates of display as the TMS coil moves thus, providing immediate feedback to the operator. This enables precise alignment of the coil with the target position. The invention offers advantages by improving treatment accuracy, reducing errors and providing user-friendly, real-time feedback for precise coil positioning. The scope of the invention extends to clinical settings, brain research and therapeutic applications where it can enhance treatment consistency and support expanded uses of TMS in medical practice and research.

A multimodal predicate diagnostic system for hospital stroke detection utilizes TEE-secured mobile devices to monitor patients in clinical environments. The system aggregates motion data using optimized sampling and cryptographic validation, applying a hybrid CNN-decision tree model optimized via transfer learning and data augmentation against an adaptive gait baseline. It computes risk confidence scores in real-time and generates alerts with hospital network integration.

An intelligent fluid monitoring cup system for healthcare settings comprises a cup body with at least 750mL capacity, double handles, and a twist-on lid providing rotational access for patients with limited mobility. The system incorporates a capacitive sensor capable of detecting fluid level changes less than 0.5mm, providing sub-millimeter precision measurement. A computing device with integrated screen displays real-time fluid intake data, NPO status indicators, and remaining allowances. The system features direct integration with hospital electronic medical records via HL7 / FHIR interoperability standards, barcode patient-device association, and nurse verification workflows. Hospital-grade hygiene protocols include sterilizable shells or disposable liner systems for multi-patient clinical environments.

1. Name of the product in this design: Fully Automated Fluorescence Analyzer. 2. Purpose of this design: To enable point-of-care testing in clinical settings, quickly obtain diagnostic results, and process samples in batches. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model. 5. Other situations requiring explanation: Part A in the main view is transparent.

The disclosure provides methods for the use of gene expression measurements to classify or identify neuroendocrine cancer in samples obtained from a subject in a clinical setting, such as in cases of formalin fixed, paraffin embedded (FFPE) samples.

An AI algorithm may be used in a clinical setting to perform one or more tasks to assist medical personnel. The results produced by the AI algorithm may affect not only patient care, but also the cost of the care. The AI algorithm may be trained on auxiliary data to incorporate the impacts on patient care and cost.

The invention discloses a cross-modal lightweight colorectal tumor segmentation method and device, and aims to solve the problems that an existing medical image segmentation model depends on large-scale annotation data, small lesions and fuzzy boundaries are inaccurately recognized, the calculation complexity is high, clinical deployment is difficult, and the like. According to the method, on the basis of an improved U-shaped network architecture, adaptive histogram equalization and Gamma correction are introduced in a data preprocessing stage to enhance boundary information of a low-contrast CT image; the encoder captures a multi-level context by adopting a multi-scale convolution and cavity convolution fusion module, and strengthens tumor edge response in combination with a boundary attention module; and meanwhile, the calculation amount is reduced by introducing rapid spatial pyramid pooling. The decoder effectively recovers details and avoids artifacts through lightweight jump connection and bilinear interpolation up-sampling. The parameter quantity of the whole model is only about 4.25 M, the reasoning efficiency and the cross-equipment generalization ability are remarkably improved while the high segmentation precision is guaranteed, and the method is suitable for a resource-limited clinical environment.

The application provides a lightweight orthodontic efficacy prediction method and system based on semantics and three-dimensional optimization, relates to the technical field of orthodontic efficacy prediction, and comprises the following steps: obtaining facial images and cephalometric data of historical orthodontic patients before and after orthodontic treatment to form a training set, constructing a structured orthodontic semantic prompt, using the pre-training diffusion model as the basis, using the training set images and the semantic prompt, and fine-tuning the prediction model through the LoRA technology; inputting the orthodontic pre-image and data of a target patient into the image to the prediction model, generating a two-dimensional facial image after orthodontic treatment, and performing three-dimensional reconstruction; optimizing the geometric consistency with the side image, and outputting a three-dimensional model for visual display. Through the combination of the LoRA fine-tuning technology, the structured medical semantic prompt and the three-dimensional geometric consistency optimization, the application can train an efficient prediction model under a small amount of paired data, reduce the calculation cost of model training and reasoning, adapt to the deployment of a clinical environment, and realize the controllability and interpretability of the generated results through the medical semantic prompt.

The present invention discloses a central venous catheter (CVC) extension device featuring a plate with a bore passing through it and an opening connecting to the bore, with two or more protrusion structures extending from the plate's second side. The opening's first end is linked to the bore, while its second end borders a section of the plate's outer perimeter edge. This device enhances the functionality and versatility of CVC extension devices, providing improved access and ease of use for medical professionals in various clinical settings.

The present disclosure deals with the biochemistry of reagents useful in the detection of ammonia in liquid samples. Specifically, the present disclosure is directed to a technical improvement of an enzyme-based test for ammonia that can be used for analysis of plasma samples taken from patients in clinical settings, among other uses. In this regard, stability of a reagent containing NAD(P)H is improved, enhancing shelf life and results in the detection of ammonia. In an exemplary reagent ammonia released as a result of NAD(P)H decay is scavenged using an enzymatic reaction to convert the ammonia using GLDH, NAD(P)H and 2-oxoglutarate, thereby forming L-glutamate, NAD(P)+ and H2O in the NAD(P)H containing reagent.

tFUS delivery and tracking systems and methods assess target location accuracy and therapy efficacy by measuring the specific and predicted downstream effects in individualistic responses to tFUS waveforms. The downstream effects include physiological, stress, mood, movement, attention measurements, subjective reports, task-based performance, etc. The measurements are performed before, during, and between tFUS sessions intermixed with optional control periods or sessions. In an embodiment, when a target brain region does not offer any immediate readouts but is surrounded by regions that may, these latter regions can be used instead for triangulation or waveform optimization. Individual and group tracking methods help identify useful measurement modalities and the expected direction and magnitude of change in response to therapy. A method that enables individualized functional targeting in a non-clinical setting by optimizing cost and complexity is described.