Tissue surface tracking of tissue features is disclosed. First surface imaged features are tracked based on the first and second time spaced images at a first wavelength. Second surface imaged features are tracked based on the first and second time spaced tissue surface images at the second wavelength. Tracking metrics are obtained based on the tracking steps. The tracking steps are combined to provide a combined tracking metric. The combined tracking metric is used in a tissue surface navigation application.

A visible-light-image physiological monitoring system with thermal detecting assistance is disclosed. The system takes a visible-light image and a thermal image of a body at the same time. A processing unit identifies a body feature of the visible-light image and determines a coordinate of the feature. In a learning mode, an initial temperature of the body feature is determined from the thermal image according to the coordinate of the body feature. After then, a physiological status monitoring mode is executed to monitor the temperature changes of the body feature and output an alarm when the temperature is determined to be abnormal. Therefore, a monitoring accuracy of the visible-light-image physiological monitoring system is increased and avoids transmitting false alarms or no alarms.

A mechanical ventilation device comprising at least one electronic controller configured to receive imaging data and transpulmonary pressure data associated with a lung of a patient; perform deformable image registration of the inhalation image and the exhalation image to produce a relative compliance or elasticity map of the lungs; convert the relative compliance or elasticity map of the lungs to a quantitative compliance or elasticity map of the lungs based on the inhale transpulmonary pressure and the exhale transpulmonary pressure; and display the information relating to or derived from the quantitative compliance or elasticity map on a display device.

Embodiments of the present disclosure provide systems and methods for training a machine-learning model for predicting emotions from received media data. Methods according to the present disclosure include displaying a user interface. The user interface includes a predefined media content, a plurality of predefined emotion tags, and a user interface control for controlling a recording of the user imitating the predefined media content. Methods can further include receiving, from a user, a selection of one or more emotion tags from the plurality of predefined emotion tags, receiving the recording of the user imitating the predefined media content, storing the recording in association with the selected one or more emotion tags, and training, based on the recording, the machine-learning model configured to receive input media data and predict an emotion based on the input media data.

A computer-implemented system and associated methods are disclosed including a device for personalized activity monitoring using the hands The device is worn about a wrist and captures images along the wrist including movement of the hands to monitor predetermined hand movements relative to an object of interest.

A wearable device consists of a smart band and a display unit. The smart band comprises a microbial biosensor, a particulate matter sensor, an enviro sensor, a single board computer, a power supply unit, a band fastener, and a set of watch adapters. The microbial biosensor detects, measures, and monitors beneficial microorganisms and pathogens in a nasal cavity, an oral cavity, or on a surface. The microbial biosensor sterilizer kills pathogens. The particulate matter sensor detects, measures, and monitors a set of suspended particles in the surrounding air comprising beneficial microorganisms, pathogens, pollen grains, dust mite allergens, and an air quality index. The enviro sensor detects, monitors, and measures environmental conditions surrounding the user. A computing system comprises a wearable device, a microbiome mobile application, a user, a mobile device, a laboratory testing facility, a cloud server, and a physician.

A system may be configured to facilitate a medical procedure. Some embodiments may: acquire a scan corresponding to a region of interest (ROI); capture an image of a patient in real-time; identify, via a trained machine learning (ML) model using the acquired scan and the captured image, a Kambin's triangle; and overlay, on the captured image, a representation of the identified Kambin's triangle.

Provided is a dynamic analysis apparatus that predicts a respiratory function value based on frame images showing dynamics of chest. The dynamic analysis apparatus includes a hardware processor that obtains a first lung size value of a removal target site and a second lung size value of a left or right lung field including the removal target site, calculates a proportion between the first and second lung size values as a size proportion, calculates a first feature amount concerning respiratory function of the left or right lung field including the removal target site and a second feature amount concerning respiratory function of the lung fields as a whole, calculates a proportion between the first and second feature amounts as a feature amount proportion, and calculates an expected rate of the respiratory function without the removal target site, based on a product of the size proportion and the feature amount proportion.

The present disclosure deals with the quickening of MRI examinations. Subjects of the present disclosure are a method, a system, a computer program product, a use, a contrast agent for use and a kit.

A computer-implemented method for obtaining thermal image data of a body part and a thermal imager are disclosed. The method comprises receiving image data of a body part, the image data including thermal image data, the thermal image data including thermal image data of the body part and background thermal image data, applying a classifier to the image data to detect a profile of the body part, the classifier being configured to use image data other than the thermal image data to perform the detection, generating a mask from said profile, and applying the mask to the thermal image data to remove the background thermal image data and obtain thermal image data of the body part.