Provided herein are systems, devices and methods for monitoring the progression of, determining and/or assessing brain related conditions in a subject based on pupil light responses (PLRs) to focal central and peripheral chromatic light stimuli, in particular, by classifying the PLR based on one or more PLR parameter values, wherein the classifying allows monitoring the progression of, determining and/or assessing the brain related condition

Patient sleep is staged using personalized circadian models built with data collected by wearable devices over daytime and nighttime hours, thus capturing a patient's personal circadian rhythms. The circadian model is used to identify sleep intervals in incoming nightly data for the patient. The identified sleep intervals are analyzed by the machine learning system which stages epochs of sleep. Methods include receiving patient heart rate data from over a plurality of circadian cycles; creating a circadian model for the patient with a defined operation for applying sleep labels to new data from the wearable device; applying the circadian model to nightly test data from the device to identify a sleep interval; and assigning, with a classifier, sleep stages to epochs of the sleep interval.

Disclosed herein are an apparatus and a method for estimating the behavior of a user based on an image converted from sensing data. The apparatus for estimating the behavior of a user based on an image converted from sensing data includes memory for storing at least one program, and a processor for executing the program, wherein the program performs acquiring sensing data measured by one or more behavior measurement devices worn by the user, converting sensing data of the user obtained for a predetermined time period into images, and estimating the behavior of the user from the images of the user based on a pre-trained model.

An example operation includes one or more of collecting, by the transport, data from a device associated with an occupant containing an amount of movement of the device and an amount of time elapsed during the movement, and determining an injury level of the occupant based on the data after a collision.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

A system and method for generating a fusion of volumetric images and surface scan images said system comprising: a processor configuring the system to: receive both a volumetric image tooth mesh and surface scan image tooth crown mesh from a same patient, registered to a similar coordinate system; segment by anatomical structure each of the registered meshes that are in common between each of the registered volumetric image tooth mesh and the surface scan tooth crown mesh; and recognize a fusion vertices for each of the segmented volumetric image tooth mesh and segmented surface scan tooth crown mesh for matching the recognized meshes; remove a surface fragment from the matched volumetric image mesh in common with the matched surface scan image mesh for removal from the volumetric image mesh; and fuse the meshes by triangulating the recognized fusion vertices.

A computer apparatus and methods generate multi-parametric color composite images from multi-echo chemical shift encoded (CSE) MRI. Some embodiments use inherently co-registered images (i.e., image maps) that are combined into a single intuitive composite color image. The color (e.g., brightness, hue, and/or saturation) reflects in part the water and fat content, and other properties, particularly T2* relaxation (related to magnetic susceptibility) of the tissue.

A method of generating a model for generating a synthetic electrocardiography (ECG) signal comprises: receiving subject-specific training data for machine learning, said training data comprising a photoplethysmography (PPG) signal acquired from the subject and an ECG signal acquired from the subject, wherein the ECG signal provides a ground truth of the subject for associating the ECG signal with the PPG signal; using associated pairs of a time-series of the PPG signal and a corresponding time-series of the ECG signal as input to a deep neural network, DNN; and determining, through the DNN, a subject-specific model relating the PPG signal of the subject to the ECG signal of the subject for converting the PPG signal to a synthetic ECG signal using the subject-specific model.

A computer-implemented method, computer program product and computing system for receiving a single-lead heartbeat waveform for a user; comparing one or more portions of the single-lead heartbeat waveform to one or more ML-generated waveform features to associate a heart health indicator with the single-lead heartbeat waveform; and providing the heart health indicator to a recipient.

Rather than rely on variation from physician to physician and limited imaging information for assessing plaque vulnerability of a patient, medical imaging and other information are used by a machine-implemented classifier to predict plaque rupture. Anatomical, morphological, hemodynamic, and biochemical features are used in combination to classify plaque.