939 results about "Non invasive" patented technology

A non-invasive physiological sensor system implemented as a smart watch or other wearable device includes an emitter for emitting light and a detector for collecting light after the light interacts with a tissue of a wearer. The system can measure light intensity at one or more radial distances to estimate attenuation parameter values that can be used to correct physiological parameter bias across subjects. In addition or alternatively, the system can determine whether a detector is positioned sufficiently proximate to an obstructing tissue (for example, an artery or vein) so as to cause inaccurate measurements by the detector.

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.

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

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.

A platform for characterizing biomolecules, biological tissue sections, viruses, or other particles of interest in a non-invasive, high-throughput, cost-effective, and label-free manner. The platform includes a merged set of imaging modalities including and capable of interferometric microscopy and wide-field or confocal microscopy (such as, for example, a wide-field super-resolution surface enhanced Raman spectroscopy, SERS), although some aspects may be employed with data sets only received from wide-field or confocal microscopy. Related devices, systems, methods, techniques, and variations to the same are also provided.

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.

The invention provides a scoliosis screening method and system, and relates to the technical field of medical artificial intelligence, human body image data on a low-light detection garment worn by a patient is collected, the human body image data is preprocessed and sent to an SSD module, the SSD module is used for predicting the position of a low-light light source, and a scoliosis detection result is obtained. The key point detection module is used for detecting human body scoliosis, high-precision detection frame information in the detection result is used as a basis, a human body posture result predicted by the key point detection module is restrained and corrected, then human body scoliosis grading is performed, a human body scoliosis detection result is displayed and fed back to a patient, high-precision detection of scoliosis is achieved, and the system has the advantages of high efficiency, non-invasiveness and light weight; the scoliosis early detection rate and grading intervention precision can be remarkably improved, and the problems that traditional medical diagnosis is low in efficiency, large in radiation risk, insufficient in precision and difficult to operate on embedded equipment with limited resources are solved.

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

The present disclosure, in some aspects, provides one or more biomarkers (e.g., plasma biomarkers or circulating biomarkers) for Facioscapulohumeral muscular dystrophy (FSHD). In some embodiments, a biomarker (e.g., plasma biomarkers or circulating biomarkers) described herein is regulated by DUX4. Methods (e.g., non-invasive methods) of using the biomarkers, e.g., for detection (e.g., early detection) and monitoring patient as well as treatment efficacy are also provided. The present disclosure, in other aspects, provides one or more biomarkers (e.g., transcriptome biomarkers) for Facioscapulohumeral muscular dystrophy (FSHD). In some embodiments, a biomarker (e.g., transcriptome biomarkers) described herein is regulated by DUX4. Methods of using the biomarkers, e.g., for detection (e.g., early detection) and monitoring patient as well as treatment efficacy are also provided.

A method for performing classification of a severity of at least one liver disease from non-invasive radiographic images is disclosed. The method comprises: providing radiographic images of slices of an abdomen of a patient; pre-processing said radiographic images by: segmenting a liver and a spleen, thus achieving a spleen binary mask and a liver binary mask per slice, and normalizing said images with each other, thus achieving normalized radiographic images per slice; for each slice, from the liver binary mask and said normalized radiographic images, extracting a liver parameter; from at least one spleen binary mask, extracting a spleen parameter; and classifying, in function of both parameters and by help of a trained Machine Learning model, the severity of the at least one liver disease between one among a group of liver disease at early stage and a group of liver disease at advanced stage.

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.

This invention relates to a non-invasive predictive kit and method for the progression of benign prostatic hyperplasia (BPH) based on prostatic fluid. The invention provides the application of FGF9 as a non-invasive predictive biomarker for the progression of BPH based on prostatic fluid, wherein the concentration of FGF9 in prostatic fluid serves as an indicator of BPH progression. The invention also provides a method for detecting the concentration of FGF9 in prostatic fluid for non-disease diagnostic purposes. Compared with existing technologies, the non-invasive BPH detection method based on prostatic fluid of this invention has advantages such as non-invasiveness, accuracy, simplicity, practicality, and scalability, and is expected to be widely used in the clinical diagnosis and treatment of prostate diseases.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention relates to a feeding intolerance burst risk early warning method and device based on multi-modal dynamic monitoring. The method comprises the steps that a gastrointestinal tract peristaltic wave image sequence under the abdominal skin surface of a patient is collected; collecting borborygmus data; collecting physical sign physiological data of the patient; monitoring behavior and posture data of the patient in real time; tracking the infinitesimal displacement and deformation of the gastrointestinal wall in the gastrointestinal tract peristaltic wave image sequence, and extracting and quantifying the speed, direction and wave crest and trough change rate of the peristaltic wave and the regularity index of the peristaltic wave; calculating a physiological deviation degree between the fusion feature at the current moment and an expected normal mode generated by the physiological feature baseline model of the patient; and inputting the physiological deviation degree, the change rate of the physiological deviation degree in the time dimension and the accumulated deviation duration into a conditional variation risk assessment model, and outputting a potential spatial distance. Through the non-invasive and highly quantified feeding intolerance risk model, the early recognition capability and clinical intervention efficiency of feeding intolerance are remarkably improved.

A histotripsy therapy system configured for the treatment of tissue is provided, which may include any number of features. Provided herein are systems and methods that provide efficacious non-invasive and minimally invasive therapeutic, diagnostic and research procedures. In particular, provided herein are optimized systems and methods that provide targeted, efficacious histotripsy in a variety of different regions and under a variety of different conditions without causing undesired tissue damage to intervening / non-target tissues or structures.

The invention discloses a preparation method and application of a granzyme B response near-infrared two-region ratiometric fluorescent probe (DCGA). The DCGA combines the NIR-II dual-emission fluorescence characteristic of rare earth ions with a ratio-type signal response mode, the sensitivity and signal stability of in-vivo fluorescence imaging are greatly improved, ultralow or enhanced F1060nm fluorescence signals are respectively shown before and after interaction with granzyme B (GzmB), F1525nm fluorescence signals are almost kept unchanged, and the DCGA can be used for detecting the in-vivo fluorescence imaging of the granzyme B (GzmB). Therefore, the self-calibration ratio fluorescence imaging GzmB activity change is realized. The DCGA is applied to tumor treatment of immunotherapy drugs, and responders and non-responders of immunotherapy can be distinguished in a non-invasive early stage through ratio fluorescence signals of the DCGA. Therefore, the fluorescent probe can be used as an effective GzmB active NIR-II ratio fluorescence imaging tool, and is expected to provide powerful support for immunotherapy research, precision medicine and personalized diagnosis and treatment.

A non-contact, non-invasive health monitoring device and method utilizing advanced artificial intelligence (AI) and machine learning techniques. This system captures real-time image data of a user's face using a high-resolution camera and processes the data to extract physiological signals, including Photoplethysmography (PPG, iPPG, and rPPG) and Ballistocardiography (BCG and iBCG). By leveraging facial landmark detection and deep learning models such as Convolutional Neural Networks (CNNs) and Transformers, the device predicts vital signs such as heart rate, respiratory rate, blood pressure, and oxygen saturation, alongside wellness metrics like stress levels and metabolic health. The device employs robust feature construction and signal processing modules to ensure accurate metrics under varying conditions, with error margins below 5%. Outputs are displayed in real time and integrated with external systems using standardized healthcare protocols. Applications include telehealth, fitness monitoring, public health screening, and automotive safety systems.

This disclosure describes examples of dual-modality imaging implementations involving both photoacoustic and fast super-resolution ultrasound localization imaging for non-invasive and non-superficial monitoring of both structural information and physiological activities / parameters in tissues. As an example, such dual-modality imaging may be particularly applied to the monitoring of structural information and physiological activities / parameters associated with the blood-brain barrier (BBB) in brain vascular structures that are subject to intervention / modulation (by, e.g., Focused Ultrasound, or FUS) for purposes of drug delivery from a blood flow to brain tissues.

A portable, multimodal dental diagnostic device for assessing the vitality of the dental pulp, consisting of: a detachable diagnostic component comprising a variety of probes, including the following: a heat probe configured to deliver controlled heat stimuli to a tooth surface and measure the heat response of the dental pulp; an electrical probe configured to deliver low-voltage electrical pulses to the tooth surface and measure the electrical impedance of the dental pulp; and an optical probe configured to assess blood oxygen saturation and microcirculation in the dental pulp using near-infrared light; a microcontroller unit configured to process sensor data from the thermal probe, the electrical probe and the optical probe, to calibrate measurements based on the oral base temperature and the ambient conditions, to aggregate multimodal sensor data and to control the operation of the thermal probe, the electrical probe and the optical probe; a cloud platform configured to receive and store aggregated sensor data, run artificial intelligence algorithms to analyze multimodal sensor data and generate diagnostic insights that include vital signs and diagnostic probabilities; a user application that is communicatively connected to the cloud platform and configured to wirelessly receive aggregated sensor data, display detailed diagnostic reports with vital signs and trends to the user, integrate with electronic health data systems, and issue alerts in case of abnormal readings; a wireless communication module connected to the microcontroller unit, configured to transmit the aggregated sensor data to the cloud platform; a display interface configured to show real-time feedback from the probes, including vitality assessments and sensor readings; and a main body containing the microcontroller unit, a rechargeable power supply and the display interface, wherein the rechargeable power supply can be charged via the current provided through a charging port on the main body, and wherein the main body is also equipped with on / off and start / stop switches; the device is configured to provide a non-invasive, quantitative real-time analysis of dental pulp health by combining physiological responses from thermal, electrical, and optical measurements.

Compositions, methods, kits, systems, and software are provided for non-invasive prenatal testing for autosomal recessive diseases. Next generation sequencing is used to sequence maternal and fetal DNA isolated from maternal plasma by probe capture. The fetal fraction of the sequencing reads for DNA isolated from maternal plasma is estimated by counting single nucleotide polymorphisms (SNPs) for which an allele is detected that is present in the paternal haplotype but absent in the maternal haplotype, based on the assumption that SNPs having a paternal allele belong to the fetal DNA. The fetal fraction is bioinformatically enriched by excluding sequencing reads over a specified length via in-silico size selection, which increases fetal genotype prediction accuracy. Parental haplotype information together with the read ratios observed at the linked SNPs is used to predict the fetal genotype at a site of a mutation linked to the autosomal recessive disease.

A method for non-invasively identifying a location of a mass or tumor including: providing a patient with a possible mass or tumor; providing a detector including one or more resonant sensors, each sensor including at least one loop resonator, at least one cavity resonator, or at least one waveguide resonator; detecting resonant frequencies at different locations on the patient's skin near locations of the mass or possible tumor; and locating the mass or tumor by noting a location of an atypical resonant frequency.

Methods for non-invasive or minimally invasive assessment of epithelial tissue structure are disclosed. Digital imaging and processing are used to identify cell locations. More specifically, an automated algorithm that may be used to identify epithelial tissue structure, and / or to specify the coordinates / locations of cells in the epithelial tissue structure, through non-invasive or minimally invasive imaging, and use of this information to extract values of epithelial structure related parameters are disclosed.

A system for examining an animal that includes a kiosk housing having an analysis zone. The system includes a persuasion delivery device in the analysis zone. The system includes a dispensing machine to dispense an animal attractant to the persuasion delivery device. The system includes a set of sensors located in the analysis zone to sense real-time data related to health of an animal. The set of sensors includes at least one sensor to sense within the analysis zone at least one biological parameter of the animal related to the health. A method is provided using the system that autonomously guides the animal to the analysis zone by a predisposition of the animal to hunt for an animal attractant so that biological parameters of the animal related to the health of the animal are sensed in a stress-free or reduced stress environment.

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 an ALS prognosis evaluation system based on UBE2J2 gene methylation and expression level, and relates to the technical field of biomedicine and bioinformatics. Comprising a data acquisition module, a methylation detection module, an expression quantity determination module, a data preprocessing module, a preliminary survival analysis module, a multivariable Cox proportional risk model construction module, an individualized risk index calculation and grouping module and a visualization and clinical application module. By combining UBE2J2 gene methylation and expression quantity analysis, peripheral blood non-invasive detection, standardized experiment and computational analysis process and visual presentation, the method is technically and remarkably superior to the prior art, high accuracy, operability, repeatability and expandability are realized, and the method is suitable for large-scale popularization and application. And the technical defects of single molecular marker, complicated operation, inaccurate prediction and the like in ALS prognosis evaluation in the prior art are overcome.