2095results about "Diagnostics using spectroscopy" patented technology

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 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 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.

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.

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.

The invention relates to the technical field of intelligent health, and discloses an intelligent health middle-aged and elderly people behavior data real-time monitoring and analysis method and system, and the method comprises the steps: collecting a biological resonance signal, a gas spectrum and a sound wave signal of middle-aged and elderly people; constructing a resonance characteristic spectrum of the middle-aged and elderly people, and analyzing physiological feedback characteristics of the middle-aged and elderly people by using the resonance characteristic spectrum; constructing an odor-behavior association map of the middle-aged and elderly people, and identifying gas feedback characteristics of the middle-aged and elderly people by using the odor-behavior association map; performing frequency domain decomposition on the sound wave signal to obtain an infrasonic frequency band and an ultrasonic frequency band, identifying internal organ vibration characteristics and body surface action characteristics of the middle-aged and elderly people, and analyzing voiceprint feedback characteristics of the middle-aged and elderly people; the physiological feedback features, the gas feedback features and the voiceprint feedback features are used for conducting behavior analysis on the middle-aged and elderly people, and a behavior monitoring report is obtained. The method can improve the reliability of behavior abnormality analysis of the elderly in smart health.

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.

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

The invention relates to the technical field of rehabilitation data prediction, and discloses a motion function rehabilitation prediction method and system based on multi-modal data, a terminal and a storage medium, and the method comprises the steps: obtaining an electroencephalogram signal, an electromyographic signal and a functional near infrared spectrum signal of a target user, pre-processing to obtain a target electroencephalogram signal, a target electromyographic signal and a target functional near infrared spectrum signal; respectively carrying out feature extraction to obtain three time-frequency features, and carrying out source positioning processing on the target electroencephalogram signal and the target functional near infrared spectrum signal to obtain brain source intensity; and fusing the three time-frequency features to obtain a target time-frequency feature, inputting the target time-frequency feature and the brain source intensity into a multi-modal data rehabilitation prediction model, and outputting a motor function rehabilitation prediction result. According to the method, the relationship between the three signals of the target user and the motion function of the target user is modeled in the time sequence, so that the long-term evolution of the motion function in the time sequence is quickly evaluated, and the accuracy of a prediction result is improved.

The invention discloses an auxiliary renal puncture device for the nephrology department, and belongs to the technical field of renal puncture, the auxiliary renal puncture device comprises a sickbed, a plurality of supporting legs are fixedly arranged at the bottom of the sickbed, fixing plates are fixedly arranged on the two sides of the sickbed, and second sliding blocks are slidably connected to one sides of the two fixing plates; the breathing frequency and amplitude of a patient are captured in real time through an infrared breathing monitoring module, and displacement data generated by breathing of the kidney are accurately obtained in combination with motion sensing modules such as a lead screw encoder and a driving gear angle encoder. The intelligent control system drives the first stepping motor and the second stepping motor to adjust the position of the portal frame based on six-dimensional force sensor data of the force sensing module, meanwhile, the angle of the puncture needle is adjusted in real time through feedback of a mechanical arm joint encoder, and dynamic compensation of kidney respiratory movement is achieved. According to the mechanism, the puncture deviation caused by breathing can be controlled within the range of + / -0.5 mm, the puncture positioning precision is remarkably improved, and poor sample quality or operation risks caused by breathing movement are avoided.

The invention relates to the technical field of multispectral imaging, in particular to a multispectral imaging fused pattern recognition image acquisition method and device, and the method comprises the following steps: obtaining a multiband substrate response value through a multispectral sensor, generating response difference distribution through moving average filtering, inputting cross-correlation analysis, extracting a space offset vector, and compensating displacement; after the signal intensity of the mark points is captured, Gaussian fitting is carried out to generate registration coordinates, triangular mesh division is carried out, offset is calculated, interpolation mapping is carried out, a registration error distribution diagram is generated, bilinear interpolation remapping is carried out to collect time sequence parameters, and a multispectral imaging data set is output. According to the method, noise is eliminated through moving average filtering, offset vectors are extracted in a cross-correlation mode, array displacement is compensated, mark points are positioned through two-dimensional Gaussian fitting, a registration error graph is constructed through triangular meshes, parameters are collected through bilinear interpolation remapping, geometric distortion is corrected, feature consistency and collection synchronism are enhanced, and the imaging resolution ratio and the signal-to-noise ratio are improved.

The invention relates to the field of medical information monitoring, and discloses a doctor monitoring bracelet for receiving infusion information of a patient, comprising: a multispectral sensing module for collecting an original physiological light intensity signal of the wrist of the patient; the motion sensing module is used for collecting motion signals; the micro-electro-mechanical motion compensation module is configured on the multispectral sensing module; the processor is electrically connected with the multispectral sensing module, the motion sensing module and the micro-electro-mechanical motion compensation module; and the communication module is electrically connected with the processor. By integrating a high-sensitivity multispectral sensing module and a micro-electro-mechanical active motion compensation system and combining an advanced signal processing algorithm, the accuracy of real-time monitoring of key vital signs of a patient in a complex infusion scene is remarkably improved, more reliable patient infusion information is provided for a subsequent multi-head infusion intelligent switching control system, and the infusion safety of the patient is improved. High fidelity of a data source is ensured, and tedious work of medical staff is reduced.

The invention discloses a digital imaging method of an ear-nose-throat examination endoscope, and relates to the technical field of medical treatment, and the method comprises the following steps: synchronously collecting a white light image, a photoacoustic signal, a stimulated Raman spectrum, pressure sensor data and a physiological signal of a target orifice through a multi-physical field probe; constructing a four-dimensional tensor bound with the anatomical features; based on a vocal cord vibration fundamental frequency harmonic characteristic optimization graph convolutional network, dynamically constructing an adjacent matrix and coupling cross-modal characteristics to generate a submucosal lesion enhanced image; a deformable convolutional network constrained by vocal cord biomechanics is adopted, and the shape of a convolution kernel is dynamically adjusted according to the relation between real-time strain and elastic modulus, so that motion artifacts caused by swallowing actions are inhibited; generating a curvature-driven asymmetric convolution kernel based on ear canal spiral geometry, and executing super-resolution reconstruction in combination with confrontation training of fractal constraint; and fusing the white light image gradient and the pressure gradient field, and outputting a three-dimensional lesion contour consistent with the anatomical structure.

The invention relates to the technical field of chronic kidney disease screening, and discloses an intelligent chronic kidney disease early screening instrument which comprises a physiological parameter detection module configured to synchronously collect multi-dimensional bio-electricity signals, optical characteristic parameters and body fluid biochemical parameters of a subject and output original signals; the multi-modal data processing module is in communication connection with the physiological parameter detection module and is configured to receive original signals, perform time domain and frequency domain conjoint analysis and construct a parameter incidence matrix; the wireless communication module is configured to realize encrypted interaction between the screening data and the cloud server and block chain evidence storage; and the man-machine interaction module is configured to display the risk level map in real time. According to the method, holographic dynamic correlation modeling of kidney function related micro-electrophysiological fluctuation and optical metabolism characteristics is realized, and a specific cross-modal pathological correlation mode of early renal injury can be effectively identified based on a time-frequency domain combined characteristic extraction and dynamic coupling algorithm.

A surgical system comprising a surgical instrument and a surgical hub is disclosed. The surgical instrument is configured to perform a surgical procedure and comprises a housing, a shaft assembly, an end effector, and a sensor configured to sense a parameter associated with a function of the surgical instrument. The surgical hub comprises a processor and a memory. The memory stores instructions executable by the processor to receive data from the sensor, wherein the data comprises the parameter associated with the function of the surgical instrument; infer a step of the surgical procedure being performed by the surgical instrument, based on the data from the sensor; receive data from a database, wherein the data from the database is associated with the step of the surgical procedure; compare a value of the parameter to the data received from the database; and control the surgical instrument based on the comparison.

Intravascular imaging devices as well as methods for making and using intravascular imaging devices are disclosed. An intravascular imaging device may include a catheter shaft including a proximal section and a distal section. The distal section may include a dual lumen region and a distal tip region coupled to the dual lumen region. The distal tip region may include a bumper tip member, a radiopaque member, and a sleeve disposed over and coupling the bumper tip member and the radiopaque member. An imaging core may be disposed within the catheter shaft.

The invention relates to the technical field of parameter monitoring, in particular to a body surface parameter monitoring method and system for meat duck breeding, and the method comprises the steps: collecting the original image data of a meat duck epidermis region, and synchronously obtaining an environment illumination intensity parameter; performing dynamic illumination compensation processing on the original image data to generate a standard illumination epidermis image; performing feather-epidermis region segmentation on the standard illumination epidermis image to generate a feather distribution mask and an epidermis exposure region coordinate set; extracting multi-dimensional physical sign parameters based on the epidermis exposure area coordinate set, wherein the multi-dimensional physical sign parameters comprise feather gap epidermis temperature, a feather coverage density index and an epidermis texture roughness coefficient; inputting the multi-dimensional physical sign parameters into a pre-trained growth state evaluation model, and outputting a physical sign anomaly probability value and a corresponding anomaly type code; according to the invention, feather dynamic shielding and ambient light interference are effectively overcome, a real-time linkage mechanism of physical sign parameters and feeding strategies is established, and the intelligent level and economic benefits of meat duck breeding are significantly 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.

Intravascular imaging devices as well as methods for making and using intravascular imaging devices are disclosed. An intravascular imaging device may include a catheter shaft including a proximal section and a distal section. The distal section may include a dual lumen region and a distal tip region coupled to the dual lumen region. A bumper tip member may be disposed along the distal tip region. A marker member may be disposed adjacent to the bumper tip member. The marker member may include an inner layer, a tie layer, and an outer layer including a radiopaque material. A sleeve may be disposed over and couple the bumper tip member and the marker member. An imaging core may be disposed within the catheter shaft.

Methods and apparatus for changing a brain state of a person from an initial brain state to a target brain state are described. The method includes receiving information characterizing the initial brain state, the information including a structural composition and a functional architecture of the brain, estimating based, at least in part, on the received information, a potential for the brain to change from the initial brain state to the target brain state, determining based, at least in part, on the received information and the estimated potential for the brain to change from the initial brain state to the target brain state, a non-invasive brain stimulation protocol, and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation protocol. The method also includes using brain information to inform the design of computational general artificial intelligence agents.

Optical patient monitoring systems are disclosed. The system may comprise an optical coupling system configured to transmit to and receive light signals from one or more locations on a subject; an optical processing system configured to generate optical data using the received light signals; and a computer programmed to receive the optical data; determine, using the optical data, at least one indicator of blood pressure; estimate an estimated blood pressure using the at least one indicator of blood pressure; and generate a report indicative of the estimated blood pressure. The at least one indicator of blood pressure comprises one or more of near-infrared spectroscopy (NIRS) data; photoplethysmography (PPG) data, diffuse correlation spectroscopy (DCS) data, speckle contrast optical spectroscopy (SCOS) data, speckleplethysmography (SPG) data, first derivative PPG data, second derivative PPG data, first derivative SPG data, second derivative SPG data, inflow (Fin) data, outflow (Fout) data, heart rate data, physiological data, and combinations thereof. Methods for estimating blood pressure are also disclosed.

The invention provides a transcranial deep electrical stimulation closed-loop regulation and control system and method based on fNIRS dynamic guidance. The method comprises the steps that a transcranial alternating current electrical stimulation module applies an electrical signal to a target deep brain region according to preset parameters to carry out electrical stimulation regulation and control; the synchronous triggering module is used for realizing millisecond-level synchronization of a stimulation task and near-infrared brain function data acquisition; the brain function monitoring module analyzes the concentration change of cortex oxyhemoglobin in real time through the multi-channel head cap; the electrical stimulation-response coupling mapping module positions a significant activation channel by adopting statistical test, and establishes an oxyhemoglobin concentration variable and electrical stimulation current amplitude quantitative model; and the dynamic parameter adjusting module optimizes the output current in real time. Personalized closed-loop regulation and control of non-invasive deep brain stimulation can be achieved, cerebral cortex blood oxygen signals are monitored in real time through near-infrared brain function imaging, stimulation parameters are dynamically adjusted, and the accuracy and individualization level of deep brain stimulation are remarkably improved.