Abstract: Disclosed are various embodiments for a dynamic flow apparatus for cardiovascular diagnosis and pre-procedure analysis of individual patients. The dynamic flow apparatus includes a three-dimensional (3D) cardiac model in an enclosed container. The 3D cardiac model can mimic an operation of an actual heart by pumping fluid through the 3D cardiac model and causing the model to expand and contrast. Data obtained from the operation of the 3D model can be used during a surgical procedure of an actual heart of an individual.

Abstract: Various examples are provided related to synthetic echocardiography. In one example, a method includes receiving surface electrocardiography (ECG) signals obtained from a patient; synthesizing, through a machine learning model, a 3D model of a heart based upon the surface ECG signals; and generating a rendering of the heart based upon the synthesized model of the heart. In another example, a system includes a wearable monitoring device that can collect and transmit surface ECG signals; and a computing device that can receive the surface ECG signals obtained from a patient using the wearable monitoring device; synthesize, through a machine learning model, a 3D model of a heart based upon the surface ECG signals; and generate a rendering of the heart based upon the synthesized model of the heart. The rendering of the heart can be displayed locally (e.g., by the computing device) or transmitted to a user device for display.

Abstract: Various examples are provided related to mHealth based risk stratification. In one example, a system includes a handheld echocardiography device that can generate ultrasound (US) images of a patient and processing circuitry comprising a processor and memory. The processing circuitry can receive the US images from the handheld echocardiography device; generate enhanced echo images from the US images using a generative adversarial network (GAN) model; and determine a major adverse cardiac event (MACE) risk for the patient based upon the enhanced echo images. In another example, a method includes receiving US images of a patient obtained with a handheld echocardiography device; generating enhanced echo images from the US images using a generative adversarial network (GAN) model; and determining a major adverse cardiac event (MACE) risk for the patient based upon the enhanced echo images.

Abstract: Machine-learned computational models can be trained to estimate echocardiogram parameters (as conventionally measured by echocardiography) from electrocardiograms and/or electrocardiogram-derived time-domain and/or time-frequency features. In some embodiments, a multi-level model architecture includes a level to derive the echocardiogram parameter estimate(s), with input features to that level being computed in a preceding level, and/or with one or more echocardiogram parameter estimate(s) flowing into a subsequent layer to compute downstream qualitative or quantitative indicators of heart function.

Abstract: Diastolic function may be assessed by operating one or more machine-learned computational models on electrocardiograms or electrocardiogram-derived features to compute quantitative diastolic indicators, including estimates of echocardiography parameters conventionally measured by echocardiography. In various embodiments, parameters derived from time-frequency transforms of the electrocardiograms are used as input to the model(s) and/or computed within the model(s).

Abstract: Diastolic function may be assessed by operating one or more machine-learned computational models on electrocardiograms or electrocardiogram-derived features to compute quantitative diastolic indicators, including estimates of echocardiography parameters conventionally measured by echocardiography. In various embodiments, parameters derived from time-frequency transforms of the electrocardiograms are used as input to the model(s) and/or computed within the model(s).

Abstract: Disclosed are various embodiments for a dynamic flow apparatus for cardiovascular diagnosis and pre-procedure analysis of individual patients. The dynamic flow apparatus includes a three-dimensional (3D) cardiac model in an enclosed container. The 3D cardiac model can mimic an operation of an actual heart by pumping fluid through the 3D cardiac model and causing the model to expand and contrast. Data obtained from the operation of the 3D model can be used during a surgical procedure of an actual heart of an individual.