3898results about "Diagnostics using spectroscopy" patented technology

A non-contact temperature measurement system for calculating estimated core body temperature is disclosed. The temperature measurement system can include a sensor that can detect the temperature of a patient and the temperature of ambient surrounding. The temperature of the patient and the ambient temperature can then be used to determine a core body temperature. The temperature measurement system includes an optical module having a light emitter and a light detector. The light emitter emits a beam of light towards the patient and the light detector detects a beam of light reflected by the patient. The reflected beam is analyzed to determine a distance between the temperature measurement system and the patient.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

A handheld medical diagnostic device integrates at least seven sensor modules, including a high-magnification camera, otoscope camera, stethoscope, infrared thermometer, EKG, pulse oximeter, body composition monitor, glucometer, and / or hematology analyzer. The device features a system-on-chip (SoC) processor for intelligent data management, edge computing, and cloud-based AI processing, while applying privacy-preserving techniques. A wireless transceiver enables data transmission and reception between the sensors, edge compute nodes, and cloud platforms. The ergonomically designed housing includes a display with a force-sensitive layer for user input and navigation. The high-magnification camera module offers 300× magnification for visualizing blood cells and skin, with an annular LED array, encryption engine, and physically unclonable function (PUF) circuit. The otoscope camera module features a narrow profile for ear, nose, and throat imaging, with an annular LED array.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The present disclosure pertains to head-mounted dual-sided photoplethysmography (PPG) systems and methods for blood flow measurement. In one embodiment, a system comprises a frame designed to be worn on a user's head, integrating a first PPG device positioned on the first side of the user's nose and a second PPG device on the opposite side. Each PPG device includes a light source and photodetector, enabling the capture of optical signals indicative of blood flow within the nasal tissue. A computer is operatively connected to both PPG devices, responsible for obtaining first and second signals from each side and processing these signals to calculate comprehensive measurements of the user's blood flow. This dual-sided configuration enhances measurement accuracy and reliability by mitigating motion artifacts and improving signal quality compared to traditional single-sided PPG systems.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention discloses a scoliosis evaluation system and device based on multi-modal information fusion, and belongs to the field of spine health. A back pressure electromyographic signal acquisition module acquires stress electromyographic information of a back muscle group; the thermal imaging module obtains a thermal imaging information graph of a human body; the plantar pressure acquisition module acquires plantar pressure information; the multi-mode data fusion module inputs the stress myoelectricity information, the thermal imaging information graph and the plantar pressure information into the encoder layer, a cross attention mechanism is adopted in the feature matching layer to fuse features from different modules, a classifier model is used for scoliosis evaluation, and classification is conducted according to the scoliosis degree. According to the application, the collaborative mechanism of the stress myoelectricity information, the thermal imaging information graph and the plantar pressure information of the back during scoliosis is utilized, multi-modal data fusion is performed on the stress myoelectricity information, the thermal imaging information graph and the plantar pressure information of the back, full-automatic evaluation is realized through a multi-modal data fusion model, the evaluation accuracy is high, and no radiation is generated.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention belongs to the field of artificial intelligence, and provides a multi-modal depression recognition system based on an MFE-CCANNN model, which comprises a data acquisition unit, a data preprocessing unit and an MFE-CCANNN model unit. The data acquisition unit synchronously acquires multi-mode data such as videos, audios, texts and fNIRS when a subject performs the same interview task. The data preprocessing unit comprises a video preprocessing unit, an audio preprocessing unit, a text preprocessing unit and an fNIRS preprocessing unit. The MFE-CCARNN model unit comprises a video, audio, text and fNIRS neural signal feature extraction module, a multi-modal feature fusion module and a classification module, and depression recognition and classification result output are achieved. The system supports four-level depression degree discrimination, is high in recognition precision, portable in deployment, high in interpretability and the like, and is suitable for psychological health screening and clinical auxiliary evaluation scenes.

The invention relates to the technical field of infant sleep nursing, solves the problem that in the prior art, the infant respiration frequency cannot be monitored in a non-contact mode, and provides a non-contact infant respiration monitoring method, device and equipment and a storage medium. The method comprises the following steps: acquiring multiple frames of real-time images in an infant sleep scene; monitoring the sleep state of the real-time image, and identifying a first target image when the infant enters the sleep state; performing image enhancement processing on the first target image, and outputting a second target image after respiratory movement enhancement; feature extraction is carried out on the second target image, and a target area related to infant respiration is recognized; according to the maximum likelihood criterion, the target area is processed, and infant respiration information is determined. The infant respiration is monitored in a non-contact mode, the accuracy of respiration frequency monitoring is improved through image collection, image enhancement processing and other modes, and intrusive monitoring on the infant is not needed.

The invention provides a blood glucose monitoring and early warning method. The blood glucose monitoring and early warning method comprises the steps of a, collecting a skin surface spectral signal I (lambda), b, collecting skin surface temperature T, humidity H, environment light intensity L and environment electromagnetic interference E parameters, c, preprocessing the spectral signal I (lambda), removing noise N (I) through wavelet transform, and d, calculating the blood glucose monitoring and early warning parameters. And the signal intensity is corrected through an ambient light intensity compensation model. According to the blood glucose monitoring and early warning method, a non-invasive acquisition mode combining near infrared spectrum and visible light spectrum is adopted, and a multi-modal regression model and a dynamic interference compensation technology are innovatively introduced, so that the problem that the precision of a sensor is influenced by skin temperature, humidity and environmental electromagnetic interference in the prior art is solved; the timeliness and the stability of signal purification are improved by utilizing preprocessing and optimization algorithms of spectral signals, such as wavelet transform, empirical mode decomposition and fast Fourier transform.

The invention belongs to the technical field of medical image assistance, and particularly relates to an auxiliary stereo imaging system and method for a complex chest trauma operation. By integrating multi-modal image data, real-time three-dimensional space capture, dynamic correction and risk score driven view adjustment, accurate assistance of the thymotomy operation process is achieved, the accuracy and safety of the operation are improved, the operation risk and operation difficulty are remarkably reduced, and the operation efficiency is improved. By acquiring and processing multi-modal image data in real time, a high-precision three-dimensional anatomical structure model containing thymus tissues, a blood vessel network and nerve distribution can be constructed, a visual surgical navigation view is provided for a doctor, meanwhile, a dynamic correction technology is adopted, thymus displacement parameters caused by thoracic cavity respiratory movement of a patient are monitored in real time, and the thoracic cavity of the patient can be accurately and accurately positioned. And on the basis, the real-time three-dimensional space data is dynamically adjusted, so that the spatial synchronization of the image data and the anatomical structure is ensured, and the navigation error caused by respiratory movement is effectively avoided.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention relates to the technical field of emotion and concentration monitoring, and discloses an fNIRS-based student emotion and concentration monitoring and feedback system, which comprises an fNIRS sensing module, which adopts a low-power-consumption wireless head-mounted device and continuously monitors blood oxygen signals of key brain regions in real time, and the spatial resolution reaches a millimeter level; the dynamic brain region weight analysis module is used for dynamically recognizing emotion and concentration degree related brain regions based on a deep learning model and automatically allocating weight parameters; and the data fusion module is used for constructing a two-dimensional neural network model, synchronously extracting spatial and temporal characteristics of emotion titer, awakening degree and concentration degree, and integrating eye movement tracking and heart rate variability data. Through high-precision sensing, dynamic brain region analysis and multi-modal closed-loop feedback, the accuracy and real-time performance of monitoring are improved, and the accuracy and the real-time performance of eye movement tracking are improved. And accurate brain region identification and signal processing can be better carried out for different individuals.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

Described herein are methods and apparatuses for characterizing a material at a distal end of an aspiration catheter using one or more sensors. The methods and apparatuses described herein may include a plurality of sensing electrodes at a distal end region of the apparatus, and a controller configured to sense contact and / or close proximity to the sensing electrodes. These methods and apparatuses may determine or detect vessel size. These methods and apparatuses may include distal contrast ports for applying contrast material distal to the aspiration catheter. In some examples these apparatuses include one or more reference electrodes near the sensing electrodes that may enhance detection of material (e.g., clot, blood vessel wall). These methods and apparatuses may include a steerable distal end region. In some cases the apparatuses and methods may include a retriever probe to assist in capturing clot material.

The invention belongs to the technical field of physiological parameter measurement and testing, and discloses a multi-mode optical non-contact health monitoring system and method. The system comprises a sensing measurement module, a data synchronous acquisition module, a micro-motion stabilization and correction module, a three-dimensional trajectory reconstruction module, a multi-modal data processing module and a physiological index calculation module. Optical imaging data are processed preferentially through a U-Net network and then fused with video micro-motion features, and the problem of multi-modal data coupling interference in a traditional method is solved. A mathematical mapping relation between blood flow parameters and physiological indexes is established through a diffusion optical spectrum mapping network, the segmented blood flow parameters are mapped to initial physiological indexes, and the time lag problem of single-modal data is solved through time sequence features extracted through a time sequence feature fusion network. And the weights of the time sequence characteristics and the preliminary physiological indexes are dynamically adjusted through a space-time attention mechanism, so that the robustness in a complex environment is enhanced, and the prediction precision and stability of a non-contact health monitoring system are improved.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

In one embodiment, a system includes a pump light source configured to produce a pump beam of light at a pump frequency and a Stokes light source configured to produce a Stokes beam of light at a Stokes frequency, where the pump and Stokes frequencies are offset by a frequency offset Ω. The system further includes a photonic integrated circuit (PIC) that includes multiple optical waveguides configured to: direct the pump and Stokes beams of light to a sample; receive a Raman signal produced by coherent Raman scattering of the pump and Stokes beams of light at the sample; and direct the Raman signal to an optical receiver. The optical receiver is configured to detect the Raman signal. The optical receiver includes a probe light source configured to produce a probe beam of light at a probe frequency, an optical detector, and an electronic circuit.

Hybrid unmixing technique (HyU) of this disclosure may provide enhanced imaging of multiplexed fluorescence labels, enabling longitudinal imaging of multiple fluorescent signals with reduced illumination intensities. This disclosure generally relates to imaging systems. This disclosure relates to hyperspectral imaging systems. This disclosure further relates to hyperspectral imaging systems that generate an unmixed color image of a target. This disclosure further relates to a hyperspectral imaging system that is configured to use a hybrid unmixing technique to provide enhanced imaging of a target. This disclosure further relates to a hyperspectral imaging system that is configured to use a hybrid unmixing technique to provide enhanced imaging of multiplexed fluorescence labels, enabling longitudinal imaging of multiple fluorescent signals with reduced illumination intensities. This disclosure further relates to hyperspectral imaging systems that are used in diagnosing a health condition.

The invention relates to a motor imagery recognition method combining EEG (electroencephalogram) and functional near infrared spectroscopy, which comprises the following steps: synchronously acquiring EEG data and functional near infrared spectroscopy signal fNIRS data under a motor imagery task through a multi-channel acquisition system, and preprocessing and enhancing the acquired two modal data. Aiming at the characteristics of the two signals, respectively extracting the time-frequency characteristics and the space-domain characteristics of the EEG and the space-domain characteristics of the fNIRS; designing a mask auto-encoder for each modal feature to carry out pre-training; and finally, carrying out feature fusion through a cross-modal cross attention mechanism in combination with parameters obtained by pre-training, and carrying out training through cross entropy loss. According to the method, the accuracy of motor imagery recognition is improved, and independent information and complementary information of EEG and fNIRS are effectively combined.

The invention relates to the technical field of vehicle safety monitoring, and discloses a non-contact type in-vehicle driver respiratory rate detection method and device. Physiological signal data are collected through a millimeter wave radar, infrared thermal imaging and a microphone array, breathing frequency band signals are extracted through self-adaptive wavelet transform, and breathing signal waveforms are reconstructed through time-frequency joint analysis. A model formed by combining a multi-channel convolutional neural network and a bidirectional gating circulation unit is adopted to extract a respiratory rhythm feature vector, interference is eliminated based on a dynamic time warping algorithm, and a respiratory frequency value is estimated by applying methods such as variational mode decomposition. And processing abnormal points and missing data through a sliding window mechanism, and outputting a stable monitoring result. The invention further provides a sensor data synchronous calibration and model online updating method. According to the invention, the breathing frequency can be detected with high precision in a complex in-vehicle environment, and the safety and intelligent level of the vehicle are improved.