1338results about "Diagnostics using spectroscopy" patented technology

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 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 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 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.

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.

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 relates to the technical field of health assessment, in particular to a traditional Chinese medicine health assessment method and system based on intelligent analysis, and the method comprises the steps: carrying out the multispectral image collection of the face and tongue picture of a user, carrying out the color gradation quantification processing and texture feature extraction of the obtained image data, and obtaining a face color gamut feature matrix and a tongue picture texture feature vector; and based on the tongue picture-complexion associated features and a syndrome description text input by the user, performing intelligent classification pre-judgment on traditional Chinese medicine syndrome types to obtain an initial syndrome label set, and based on user physique data, correcting the initial syndrome label set to obtain a syndrome feature fusion vector. Through the health fluctuation index and the health trend early warning signal, the change of the health condition of the user can be dynamically evaluated, and a personalized health intervention scheme is generated on the basis, so that the fluctuation of the health condition can be reflected in real time, intervention measures can be taken in time, and potential health risks can be prevented.

A muscle probe is provided for obtaining electromyography data and optical spectroscopy data from muscle tissue. The muscle probe comprises an elongate needle having an outer wall surrounding a needle interior, the needle interior comprising: a core electromyography electrode; and one or more optical fibres; wherein the needle is arranged to be inserted into a muscle, and further arranged to detect electrical activity from the muscle; and wherein the one or more optical fibres are arranged to direct incident light from a light source toward a target area of the muscle, and further arranged to receive scattered light from the target area. The present disclosure aims to provide a muscle probe to improve the diagnostic pathway for patients with neuromuscular disorders, by developing a minimally invasive bedside test of muscle health.

The invention discloses an ultra-wide-spectrum endoscopic imaging device capable of being used for short-wave infrared fluorescence imaging, which is used for in-vivo minimally invasive imaging and comprises an illumination module, a wide-spectrum image transmission module, an imaging module and an image processing module. The illumination module consists of a light source and a light guide optical fiber, and is used for generating illumination light from ultraviolet to short wave infrared band and transmitting the illumination light to the wide-spectrum image transmission module; the wide-spectrum image transmission module consists of a few-interface optical imaging channel and a light guide illumination channel, and is used for illuminating an imaging target, exciting target fluorescence and collecting image signals from ultraviolet to short-wave infrared bands; the imaging module is composed of an optical filter and an imaging camera, and the imaging camera can be a wide spectral response image sensor or can be formed by combining cameras working in different wavebands and is used for converting optical images in different wavebands into image data; and the image processing module is used for registering and fusing image data of different wavebands in real time and displaying the image data.

The invention relates to the technical field of medical instruments, and discloses a breast cancer sentinel lymph node positioning system and method, and the system comprises a data collection subsystem, a space positioning subsystem and a central processing subsystem. The method comprises the following steps: acquiring an anatomical structure image, a radioactivity counting rate and fluorescence intensity after performing primary fluorescence, radioactivity and secondary pulse type fluorescence tracer injection in sequence; establishing a global coordinate system and generating an anatomical region of interest; constructing a weighted directed topological graph based on the initial fluorescence data, and obtaining a baseline flow velocity; extracting radionuclide dynamic characteristics based on the radioactive data; a functional flow rate attenuation coefficient is calculated based on the second pulsed fluorescence data. And a multi-factor scoring model is applied, topological information, dynamic characteristics and functional flow velocity attenuation coefficients are fused, and a comprehensive score is calculated to identify the sentinel lymph node. According to the method, the lymphatic flow topological structure, nuclide dynamics and functional flow velocity information are combined, and the positioning accuracy and reliability are improved.

A method for predicting a cardiac index (CI) for a patient includes receiving a set of a measurements from the patient that characterizes an anatomical condition, a physical condition, a physiological condition, or a combination thereof. The method also includes receiving a non-invasive physiological measurement from the patient. The method also includes performing feature extraction on the non-invasive physiological measurement to produce one or more extracted features. The method also includes combining the set of measurements and the extracted features to produce a dataframe. The method also includes predicting the CI for the patient. The CI is predicted using a prediction model based upon the dataframe.

The present disclosure provides apparatus and methods to both infer stent expansion (vessel expansion or compliance) and probability of procedural success (vessel patency) from pretreatment diagnostic imaging at the point-of-care and also train an inference model to generate such an inference, thus allowing a physician to choose with greater certainty an optimal treatment tool and treatment protocol for treating a vessel of a patient.

The invention discloses a wound tissue imaging and auxiliary diagnosis system, which belongs to the technical field of wound tissue detection and comprises a hardware platform and an upper computer system. The hardware platform comprises a data acquisition module, a preprocessing and fusion module and a communication and control module; and the upper computer system is used for receiving the protocol packet and carrying out data analysis, coordinate system alignment and multispectral image waveband registration. According to the wound tissue imaging and auxiliary diagnosis system, a heterogeneous computing architecture is adopted, FPGA assembly line processing and ARM control are combined, power consumption and the size are remarkably reduced, and high-precision wound detection and real-time decision support are achieved through multispectral and three-dimensional data fusion, hardware acceleration preprocessing and AI driving analysis.

A system, apparatus and method directed to determining a temperature within a patient body, including an optical fiber with one or more core fibers. The system can include a console having non-transitory computer-readable medium storing logic that, when executed, causes operations of providing an incident light signal to the optical fiber, receiving a reflected light signal of the incident light, processing the reflected light signal to determine a temperature within the patient body near a measurement region. The method may include determining a location of a distal tip of the optical fiber within the patient body at least based on the temperature.

A laser can be controlled based on different tissue compositions, such as in real time. After a first time period, a first composition of a in vivo target site can be identified. Based on the first composition, a plurality of lasers can be controlled to emit light at a first wavelength where controlling includes activating a first combination of the plurality of lasers. After a second time period, a second composition of the in vivo target site different from the first composition can be identified. Based on the second composition, a plurality of lasers can be controlled to emit light at a second wavelength, such as can include activating a second combination of the plurality of lasers. The first combination of the plurality of lasers can be different from the second combination of the plurality of lasers.

A method of displaying an operational parameter of a surgical system is disclosed. The method includes receiving, by a cloud computing system of the surgical system, first usage data, from a first subset of surgical hubs of the surgical system; receiving, by the cloud computing system, second usage data, from a second subset of surgical hubs of the surgical system; analyzing, by the cloud computing system, the first and the second usage data to correlate the first and the second usage data with surgical outcome data; determining, by the cloud computing system, based on the correlation, a recommended medical resource usage configuration; and displaying, on respective displays on the first and the second subset of surgical hubs, indications of the recommended medical resource usage configuration.

A sensor module for Raman spectroscopy includes a sensor package which encloses an application specific integrated circuit (ASIC), a light emitter arrangement, a light detector arrangement and a filter arrangement. The light emitter arrangement is electrically connected to the ASIC and operable to emit light with multiple excitation wavelengths to excite Raman scattering in an external probe to be placed outside of the sensor module. The light detector arrangement is operable to generate sensor signals from incident light emitted back from the external probe due to the Raman scattering. The filter arrangement is operable to filter the incident light according to a target passband. The ASIC is operable to drive the light emitter arrangement at the excitation wavelengths to shift a Raman spectral band of the external probe into the passband of the filter arrangement.

The invention provides an activatable head and neck cancer diagnosis and treatment integrated nanoprobe and a preparation method and application thereof.The preparation method includes the steps that according to an original data matrix, an adaptive spectrum correction algorithm is applied to eliminate background noise of an instrument, and when the relative fluctuation of spectral intensity exceeds a preset threshold value by 5%, a sliding window filtering technology is adopted, the window size is 5 nanometers, and the spectrum intensity is obtained; generating a de-noised spectral data set; measuring the pH value of the head and neck cancer tissue microenvironment by using a pH value sensor, recording the emission peak position change of the near-infrared quantum dots when the pH value is less than 6.5 in combination with the de-noising spectral data set, analyzing the frequency band displacement by adopting a Gaussian fitting algorithm, and generating a mapping data set of the pH value and the frequency band displacement; and aiming at the activation spectrum fingerprint data set, applying a dynamic spectrum matching algorithm, comparing with the spectrum response characteristic data set, calculating a cosine similarity coefficient, and when the similarity exceeds 0.85, determining the activation state and the tissue penetration depth of the nanoprobe, and generating a diagnosis and treatment parameter adjustment instruction set.

Various example of the present disclosure provide sensing apparatuses configured for wearable and wireless use for deep tissue physiological monitoring. The sensing apparatuses may be embodied by a thin flexible patch configured to conform with a skin surface of a subject. A sensing apparatus may include a plurality of microneedles oriented to extend towards and penetrate into the subject to a shallow depth. The microneedles may be configured as waveguides for a given sensing modality (e.g., light, ultrasound), such that sensing wave signals propagate to deep tissues. For the sensing, the sensing apparatus includes waveform generators (e.g., light-emitted diodes) and waveform detectors (e.g., photodiodes). Machine learning models may be used to process and denoise sampled data from the waveform detectors and to generate accurate and reliable physiological measurements, including heart rate, respiratory rate, pulse intensity, respiratory intensity, blood oximetry, tissue oximetry, blood flow rate, and / or the like.

The invention relates to the technical field of biomedical engineering. The invention discloses a multi-physiological parameter detection method and device based on an automatic multi-exposure control algorithm, terminal equipment and a storage medium. The accuracy of detecting multiple physiological parameters can be improved. The method comprises the following steps: carrying out video recording processing on a plurality of parts of a target user by adopting a target sensor to obtain an initial video; performing exposure time calculation processing on the initial video by adopting an automatic multi-exposure control algorithm to obtain target exposure time corresponding to each part; when the target sensor continues to carry out video recording processing on the multiple parts, the exposure time of the target sensor is alternately switched according to the time sequence based on all the target exposure time, and images captured by the target sensor after each switching are sequentially integrated to obtain a target video; and performing physiological parameter detection processing on the target video to obtain physiological parameters corresponding to each part.

The invention discloses a self-adaptive wearing and monitoring method of an intelligent drainage monitoring system, and relates to the technical field of medical intelligent wearing, and the method comprises the steps: collecting the drainage part data of a patient through multi-modal biological characteristics, optimizing the wearing parameters through a particle swarm algorithm, and driving a shape memory polymer to adjust the equipment fitting degree, biochemical indexes, pressure and flow of drainage liquid are detected in real time through a micro-fluidic chip and various sensors, intelligent early warning of abnormity is achieved in combination with a self-adaptive neural network, parameters such as drainage negative pressure are automatically adjusted according to physiological signals of a patient, risks are predicted through a digital twinborn model, and a maintenance scheme is generated. According to the intelligent drainage monitoring system, self-adaptive wearing is achieved, and the comfort level of a patient is improved; real-time accurate monitoring and intelligent early warning are achieved, and medical safety is guaranteed; drainage parameters are automatically adjusted according to patient states, and treatment efficiency is improved; the intelligent dredging and anti-infection functions are achieved, the medical risk and cost are reduced, and the clinical application value is high.

Aspects relate to apparatuses and methods for individualized polygraph testing. The apparatus including at least an interface configured to communicate questions to the user, at least a sensor configured to detect biofeedback signals as a function of a biofeedback of a user, wherein the biofeedback is associated with at least an answer to at least a question, and at least a computing device including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive the biofeedback signals, generate a user state classifier, train the user state classifier as a function of a user state training set, wherein the user state training set includes biofeedback signals correlated to answers of known veracity, and classifying a biofeedback signal of the biofeedback signals to a user state as a function of the user state classifier.

The invention, which belongs to the technical field of medical image processing and computer vision, discloses a multispectral imaging-assisted post-radiotherapy skin injury early detection system comprising a multispectral data acquisition module, a deep tissue feature extraction module, a three-dimensional lesion segmentation module and a space-time tracking evaluation module. The subcutaneous 2.5 cm depth tissue information is obtained through multispectral imaging at the wave band of 400-1350nm, blood perfusion, melanin concentration, collagen structure and other physiological parameters are inversed based on the radiation transfer theory, a three-dimensional medical image segmentation algorithm is adopted to achieve three-dimensional accurate segmentation of an injury area, a spatio-temporal evolution model is established to predict the injury development trend, and the damage development trend is predicted. The radioactive skin injury can be detected in the subclinical period, the detection time window is advanced by 5.2 days on average, the occurrence rate of severe dermatitis is reduced by 65%, and a basis is provided for clinical timely intervention.

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 multi-mode human body recognition system and method based on photoelectric metasurface and radar fusion, and belongs to the technical field of optoelectronics and radar perception. According to the system, optical edge enhancement is realized through a phase programmable metasurface, a continuous wave radar and a frequency modulation continuous wave laser radar are combined to obtain a micro-Doppler spectrum and a three-dimensional point cloud, and a near-infrared reflection spectrum is collected at the same time. According to the algorithm, a cross-modal Transform-GAT architecture is adopted to fuse multi-source features, edge extraction is optimized through a self-adaptive kernel function, and low-power-consumption edge reasoning is achieved through a Mach-Zehnder interference device or a ReRAM chip. Experiments show that the recognition accuracy of the method in complex scenes such as multi-target shielding and low light is remarkably superior to that of a single-mode scheme, the method has the edge deployment capability with delay smaller than or equal to 25 ms and power consumption smaller than or equal to 4 W, and an efficient solution is provided for multi-scene human body dynamic recognition and health monitoring.

Methods, systems, and devices for operating a wearable device are described. A wearable device may include an optoelectronic-transmitter configured to output an optical signal with a narrow angular spread. The optoelectronic-transmitter may be at least partially covered with a material protrusion that has at least one dimension based on the angular spread. The wearable device may include an aperture within an inner surface of a housing of the wearable device. The aperture may be configured to enable propagation of the optical signal through the housing, and a width of the aperture may be based on the angular spread such that the aperture permits propagation of the optical signal within the angular spread. An optoelectronic-detector configured to receive the optical signal may be disposed at a distance from the optoelectronic-transmitter that is based on the angular spread.

An embodiment of a spectrophotometric device for measuring blood oxygen saturation includes a light source, a detector for detecting a light signal reflected by a subject's tissue, an electronic data processing unit to calculate blood oxygen saturation based on the light signal, and a motion sensor for detecting a motion signal indicative of motion of the light source and / or the light detector. The electronic data processing unit may discard a light signal measured by the detector if the motion signal detected by the motion sensor exceeds a predetermined threshold value. An embodiment of a method for measuring blood oxygen saturation includes emitting a light signal into a subject's tissue, detecting the light signal reflected by the tissue, calculating blood oxygen saturation using the light signal measured by the detector, and detecting a contact signal indicative of contact between a subject's skin and the light source and / or the detector.

The invention discloses a patient venipuncture auxiliary guiding device based on intelligent visual identification, the device is provided with a limb placing plate, the limb placing plate comprises a bottom plate and a silica gel lining plate, and a comfortable placing position can be provided for the limbs of a patient. The linear guide rail is installed on the right side of the top of the limb placing plate. The electric control sliding block can slide on the rail. The Z-axis lifting mechanism is connected with the electric control sliding block, the servo motor drives the lead screw to rotate, and the movable sleeve drives the right-angle type blood vessel imaging mechanism to ascend and descend in the vertical direction. The right-angle type blood vessel imaging mechanism is composed of two supports, a top blood vessel imager and a side blood vessel imager are embedded in the supports, imaging ports face the upper surface and the side face of the limb to be punctured respectively, and the direction and the depth of blood vessels can be clearly shown. In addition, a controller is installed on the side wall of the bottom plate and can control an electric control linear guide rail, an electric control sliding block and a servo motor, medical workers are assisted in accurately finding the blood vessel position, and the success rate of venipuncture is increased.