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.

Methods and systems are provided for predicting cardiac arrhythmias based on multi-modal patient monitoring data via deep learning. In an example, a method may include predicting an imminent onset of a cardiac arrhythmia in a patient, before the cardiac arrhythmia occurs, by analyzing patient monitoring data via a multi-arm deep learning model, outputting an arrhythmia event in response to the prediction, and outputting a report indicating features of the patient monitoring data contributing to the prediction. In this way, the multi-arm deep learning model may predict cardiac arrhythmias before their onset.

A system for generating hyperspectral imaging data for measuring biochemical compositions is disclosed which includes a spectral imaging device adapted to acquire one or more hyperspectral linescan images, an optical imaging device with a red-green-blue (RGB) sensor adapted to acquire an RGB dataset, a processor adapted to co-locate a plurality of pixels in the RGB dataset vs. a corresponding plurality of pixels of the one or more hyperspectral linescan datasets, establish a transformation matrix utilizing the plurality of co-located pixels, apply the transformation matrix to the RGB dataset to thereby generate the hyperspectral dataset, and analyze the generated hyperspectral image dataset to determine the biochemical compositions.

Devices, systems, and methods for monitoring respiration using surface temperature, humidity, air pressure, carbon dioxide gas sensors, pulse oximetry sensors and electromyography sensors, and/or acceleration sensors to obtain information related to respiration rate (RR), exhalation/inhalation strength, exhalation/inhalation volume, exhalation/inhalation acceleration, and/or exhalation/inhalation regularity.

A non-contact facial blood pressure measurement method based on 3D CNN is disclosed, which belongs to the technical field of computer vision. The method includes the following steps. S110: collecting an actual face video sample and training a blood pressure prediction model based on face images using 3D CNN neural network. S120: obtaining a face video in real time through a HD camera. S130: recognizing face key points in the face video obtained in S120 through dlib face recognition model, selecting a face region of interest, and extracting face images from the region. S140: performing a wavelet transform operation on the face images extracted in S130 to remove noise. S150: inputting seven consecutive frames of the face images into the 3D CNN blood pressure prediction model trained in S110 to obtain a blood pressure value of the measured person. The disclosure realizes non-contact facial blood pressure measurement.

A method and system for detecting spinal stenosis is provided. The method may receive image data corresponding to a spine region of a patient. The method may also identify a spinal cord in the image data. The method may determine at least one compression of the spinal cord and may mark an anatomical element proximate to a location of the determined at least one compression to yield at least one marking. The method may generate a decompression plan based on the at least one marking.

A respiratory status examination apparatus and method, and a sleep disorder control device and method are proposed. The apparatus may include at least one image capturing unit that is movably arranged to adjust a distance with respect to a subject and configured to obtain a thermal image by photographing the subject. The apparatus may also include a motion sensor unit configured to detect a motion of the subject to generate motion information, and a temperature information extracting unit configured to specify at least one examination region from the thermal image obtained by the image capturing unit and extract temperature information from the examination region. The apparatus may further include a respiratory status examining unit configured to determine a respiratory status of the subject based on the temperature information extracted by the temperature information extracting unit and the motion information generated by the motion sensor unit.

Systems and methods for providing a universal platform for at-home health testing and diagnostics are provided herein. In particular, a health testing and diagnostic platform is provided to connect medical providers with patients and to generate a unique, private testing environment. In some embodiments, the testing environment may facilitate administration of a medical test to a patient with the guidance of a proctor. In some embodiments, the patient may be provided with step-by-step instructions for test administration by the proctor within a testing environment. The platform may display unique, dynamic testing interfaces to the patient and proctor to ensure proper testing protocols and accurate test result verification.

A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.

The present invention relates to a device, system and method for less obtrusively determining a tissue characteristic of a subject, the device comprises a first control unit (11) configured to control an electromechanical transducer (31) by a first control signal (21) to transfer mechanical waves varying in a frequency range or with varying frequency content to an exposed tissue area of the subject; a second control unit (12) configured to control an electromagnetic radiation emitter (32) by a second control signal (22) to emit electromagnetic radiation towards the exposed tissue area of the subject; a radiation signal input (13) configured to obtain a radiation signal (23) indicative of electromagnetic radiation reflected from the exposed tissue area of the subject; and a processor (14) configured to determine a tissue characteristic signal (24) indicative of a tissue characteristic of the exposed tissue area of the subject derived from a frequency response or a frequency transfer function obtained from the obtained radiation signal in said frequency range or for said varying frequency content.