A remote photoplethysmography (RPPG) system for estimating vital signs of a person is provided. The RPPG system is configured to receive a set of imaging photoplethysmography (iPPG) signals measured from different regions of a skin of a person. The RPPG system is further configured to determine frequency coefficients at the frequency bins of the quantized frequency spectrum of the measured iPPG signals by minimizing a distance between the measured iPPG signals and corresponding iPPG signals reconstructed from the determined frequency coefficients, while enforcing joint sparsity of the determined frequency coefficients subject to the sparsity level constraint, such that the determined frequency coefficients of different iPPG signals have the non-zero values at the same frequency bins; and output one or a combination of the determined frequency coefficients, the iPPG signals reconstructed from the determined frequency coefficients, and a vital sign signal corresponding to the reconstructed iPPG signals.

A diagnostic system, method, and a computer-readable storage medium for determining a severity of a gastric condition, such as gastritis, in a subject are disclosed. The diagnostic system includes a processor that obtains an image of a stomach of the subject collected by an endoscope. The processor can compare the subject image with reference normal and abnormal stomach images. If the subject image is abnormal, the processor can generate a subject abnormality image indicative of the differences between the subject image and the normal image. By comparing the generated subject abnormality image with reference abnormality images representative of different severity levels of gastritis, the processor can diagnose the subject as having a particular severity level of gastritis.

A method for predicting brain disease state change is disclosed. The method includes acquiring test images obtained by capturing a portion of a human brain at a time interval, performing a pre-processing procedure of converting the test images into test voxels configured to be processed for image analysis, wherein a respective test voxel of the test voxels is composed of three-dimensional voxel units, mapping first and second test voxels selected from the test voxels acquired from a patient, with each other on a three-dimensional voxel unit, wherein the first test voxel is acquired at a first time-point and the second test voxel is acquired at a second time-point, generating a voxel-based data-set based on the mapped first and second test voxels, extracting a change in the test voxels using a deep neural network, and generating a state change probability model based on the change in the test voxels.

Systems and methods for assessing a disease are provided. Medical imaging data of lungs of a patient is received. The lungs are segmented from the medical imaging data and abnormality regions associated with a disease are segmented from the medical imaging data. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality regions. The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia.

A system and a method for measuring a maturation stage of a biological organ are based on quantitative MR maps for the organ. The method includes acquiring with a first interface and for a subject, a quantitative MR map for the organ. The quantitative MR map includes voxels each characterized by a quantitative value. The quantitative value of each voxel represents a measurement of a physical or physiological property of a tissue of the biological organ for the voxel. The method also includes applying to the quantitative map a trained function to estimate the subject organ maturation stage, and the trained function outputting an age. The method provides with a second interface the maturation stage of the organ of the subject as being the output age.

Systems for monitoring respiratory function of a person include a garment configured to be worn on a torso of the person. The garment includes one or more reference patterns positioned on the anterior torso of the person when the garment is worn. A camera is used to capture, at a first time, at least one image of the person immediately prior to inhalation, and immediately prior to exhalation, and to capture, at a second time, at least one image of the person immediately prior to inhalation, and immediately prior to exhalation. A computing device is used to transmit the images to a remote computing device for analysis.

The subject matter disclosed herein is generally directed to methods and systems for generating, projecting, and displaying a patient's disease state based on the patient's medical images wherein the projected disease state can be compared to the patient's present state of health to provide data and material for patient education, healthcare provider education, clinical practice, and medical research.

A non-transitory computer-readable storage medium storing a program causing a computer to perform the following, obtaining a radiation moving image in which a chest portion of a subject when the subject breathes is shown; analyzing a dynamic state of the chest portion based on the radiation moving image obtained in the obtaining; and extracting impedance regarding breathing based on an analysis result obtained in the analyzing.

Various embodiments are directed to video-based deep learning evaluation of cardiac ultrasound that accurately identify cardiomyopathy and predict ejection fraction, the most common metric of cardiac function. Embodiments include systems and methods for analyzing images obtained from an echocardiogram. Certain embodiments include receiving video from a cardiac ultrasound of a patient illustrating at least one view the patient's heart, segmenting a left ventricle in the video, and estimating ejection fraction of the heart. Certain embodiments include at least one machine learning algorithm.

An embodiment provides a method including obtaining, using a camera system, imagery of one or more individuals in an environment; analyzing, using a processor, the individual data using a trained model to identify individuals and recognize gestures that are indicative of sickness behavior; presenting, using a display device, information about individuals that have displayed sickness behavior above a calculated threshold.