92 results about "Clinical settings" patented technology

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.

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.

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 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.

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.

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 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.

Embodiments of the present disclosure relate to customized machine learning training of radiotherapy clinics. Disclosed herein are methods for selecting and preparing patient data to facilitate the employment and custom training of machine learning models in a clinical environment, particularly for radiotherapy treatment planning. The disclosed embodiments simplify the customized training process through an automated workflow that includes pre-filtering patient metadata, retrieving related DICOM files, optional data anonymization, and generating training data. And organizing the data 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 the clinical workflow, enabling clinics to train and implement predictive models replicating specific clinical practices, thereby improving treatment accuracy and improving patient outcomes.

The invention discloses a tumor personalized treatment method based on AI analysis of molecular chaperone autophagy characteristics, and relates to the technical field of medical artificial intelligence and tumor treatment. The method at least comprises the following steps: S1, data acquisition and preprocessing: collecting multi-modal data, labeling the multi-modal data, and increasing diversity through an image enhancement technology, the image enhancement technology at least comprises rotation, zooming and color disturbance, and data standardization is carried out; according to the invention, through multi-modal data fusion, the comprehensiveness and accuracy of CMA feature analysis are improved; the LSTM is used for processing dynamic data, the time sequence change of the CMA is captured, and the practicability of the model in a clinical environment is enhanced; transfer learning and image enhancement are adopted, so that the dependence on large-scale annotation data is reduced, and the model robustness is improved; finally, individuation of tumor treatment is achieved, side effects are reduced, and the curative effect is improved.

The embodiment of the invention provides a puncture navigation method and device, computer equipment and a storage medium, and relates to the technical field of medical instruments. The method comprises the following steps: acquiring three-dimensional image data obtained by scanning a calibration plate by medical imaging equipment, and acquiring a two-dimensional image of the calibration plate by navigation image acquisition equipment; determining a first transformation relation based on the three-dimensional image data; based on the two-dimensional image and the internal reference of the navigation image acquisition equipment, determining a second transformation relation; determining a projection transformation matrix between the medical image coordinate system and the navigation coordinate system according to the first transformation relation and the second transformation relation; acquiring a puncture path planned in the medical image space; mapping the puncture path into a navigation coordinate system by utilizing the projection transformation matrix, and rendering to generate a virtual puncture needle model; and performing fusion display on the virtual puncture needle model and an image which is acquired by navigation image acquisition equipment in real time and contains a real puncture needle. Therefore, stable and accurate real-time guidance can be provided for doctors in a conventional clinical environment.

To support efficient use of RWD (RealWorldData).SOLUTION: The computer system is accessibly connected to a first database that stores clinical data acquired at a clinical site and including information on a treated patient as an item, and a second database that stores non-clinical data acquired for research purposes and including information on a treated patient group as an item. The computer system selects a predetermined number of pieces of clinical data from the first database, generates comparison data by aggregating the selected pieces of clinical data, executes statistical analysis for analyzing a difference between items using the non-clinical data and the comparison data having the same treatment content, and records a result of the statistical analysis.SELECTED DRAWING: Figure 9

The application provides a medical device alarm method and system, which comprises the following steps: determining a basic threshold according to a physiological parameter of a patient using a medical device in a time sliding window; determining an environment compensation factor according to a clinical environment of the patient at a current time point, which is after the time sliding window; determining a clinical adjustment factor according to a clinical stage of the patient using the medical device at the current time point; determining a threshold of the physiological parameter according to the basic threshold, the environment compensation factor and the clinical adjustment factor; comparing the physiological parameter of the patient at the current time point with the threshold of the physiological parameter, and performing an alarm according to a comparison result. The application realizes dynamic adjustment of an alarm threshold, and adapts to changes of physiological parameters and clinical environments of different individuals.

In a computer-implemented method for generating training data for a machine learning model for use in a clinical setting, sensor data from at least one sensor present in the clinical setting is received. Situational information is received, which depends on a change in the state of a device in the clinical setting and / or an action by a person in the clinical setting. At least a portion of the sensor data is automatically annotated using this situational information to generate the training data.

This utility model relates to a negative pressure drainage device for preventing backflow, comprising a mounting body with a hollow tubular structure. The mounting body has a forward mounting area and a reverse mounting area at its two ends. The component preventing backflow of drainage fluid includes an annular fixing seat circumferentially fixed inside the hollow tubular structure. One end of a movable stop is connected to the side of the annular fixing seat near the reverse mounting area. The movable stop is located between the annular fixing seat and the reverse mounting area. When the pressure in the reverse mounting area is lower than that in the forward mounting area, the movable stop moves to open the inner circle of the annular fixing seat, connecting it to the drainage channel. When the pressure in the reverse mounting area is higher than or equal to that in the forward mounting area, the movable stop moves back to close the opening formed by the inner circle of the annular fixing seat. This utility model has a simple structure, is easy to use, and can handle sudden situations in clinical settings. It eliminates the need for manual opening and closing, promptly preventing backflow of drainage fluid and reducing the burden on family caregivers.

The invention discloses a children pneumonia screening method and system based on multi-modal deep learning, and is applied to the technical field of artificial intelligence assisted medical diagnosis. Comprising the following steps: acquiring respiration audio data and structured clinical medical history data of a to-be-screened child; converting the respiratory audio data into a time-frequency graph, and inputting the time-frequency graph into a pre-trained feature extractor to extract a high-dimensional acoustic feature vector; screening key clinical indexes in the structured clinical medical history data based on a feature selection algorithm, and inputting the key clinical indexes into a pre-trained multi-layer perceptron model to obtain clinical feature vectors; and carrying out weighted splicing on the high-dimensional acoustic feature vector and the clinical feature vector, and inputting the spliced high-dimensional acoustic feature vector and the clinical feature vector into a classification multilayer perceptron model to obtain the pneumonia prediction probability of the to-be-screened child. According to the method, the problem of data scarcity is solved through a two-stage transfer learning strategy, and the accuracy, specificity and generalization ability of children pneumonia screening in different clinical environments are remarkably improved by simulating the comprehensive diagnosis and treatment thinking of clinicians.

Disclosed herein are shelf-stable compositions based on synthetic gene networks and / or cell-free systems that are lyophilized on a solid support. The compositions can be easily transported and stored for a period of time, and activation can be done by simply adding water. Methods of use are also disclosed herein, including, but are not limited to, sensing and a variety of logic functions. The invention permits straightforward, sterile and abiotic distribution of synthetic biology-based technology to clinical settings, food processing and industry, the military and consumer products.

The present disclosure relates to systems and methods for monitoring a patient in a clinical setup environment by using non-contact multi-modal imaging. For example, based on receiving imaging data, a computing device may identify an object and a region of interest formed by the object. The computing device may then cause a sensor to capture first sensor data at a first time instant and capture second sensor data at a second time instant, where the sensor data is associated with the region of interest. 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 an alert indicative of the second sensor data and send the alert indicative of the second sensor data to an additional computing device.

The present invention provides a full-stack medical imaging foundation model, re for performing medical image segmentation, classification and localization using a unified language-image correlation mechanism. The present invention leverages pretrained language-guided associations to automatically recognize and localize disease targets without requiring medical imaging knowledge or manual box-prompting. The invention enables comprehensive clinical tasks across multiple disease classes and various medical imaging techniques, improves segmentation efficiency, and reduces human input burden. With operability through human language-only prompts by non-radiology specialists, the present invention improves real-world applicability and enables deployment of foundation model-based segmentation in diverse clinical environments.

A hardware-anchored orchestration system for autonomous GenAI agents in clinical settings, implementable in ASIC or FPGA fabric to ensure deterministic enforcement independent of software execution layers. The system utilizes a hardware-isolated ethical supervisor—comprising a HSM or TPM—to monitor agentic workflows against human-configured safety thresholds stored in an ethical guardrail manifest in a silicon vault. Hardware-based logic gates detect statistically anomalous token-level entropy as a causal indicator of hallucination, and bias monitors evaluate equity thresholds against manifest-defined fairness indices. If a safety breach is detected, a hardwired interlock circuit asserts a non-maskable interrupt to block the agent's output before it is committed to the clinical record. The architecture supports multi-agent quorum verification and cryptographic provenance anchoring, ensuring autonomous agentic actions remain compliant with clinical regulatory standards via hardware-verified human oversight and zero-knowledge compliance verification.

Methods for non-invasively measuring blood pressure using multi-wavelength photoplethysmography (PPG) signals obtained from peripheral blood vessels. A wearable device is calibrated using PPG signals measured distal to the wearable device and analyzed in order to create a cardiovascular profile of a wearer. After calibration, windows of PPG signals are analyzed to provide a blood pressure estimation, based on the created profile. The current invention utilizes single-site measured PPG signals in order to make a prediction of the wearer's blood pressure. By using a combination of multi-wavelength PPG waveform features and a range of vital signs calculated by the wearable device, the device is able to provide a more accurate and robust prediction of blood pressure compared to existing methods. This has important implications for clinical settings where frequent and remote monitoring of blood pressure is necessary for managing a range of health conditions.