Abstract: Systems and methods for predicting a future cardiovascular event are provided. Electrocardiogram waveform data can be acquired and utilized in a trained computational model to predict a future cardiovascular event. Clinical interventions, clinical surveillance, and clinical treatments can be performed based on a future cardiovascular event prediction.

Abstract: 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.

Abstract: Embodiments herein describe systems and methods for automated clinical image quality assessment and uses thereof are provided. Many embodiments provide instructions and feedback for clinically relevant image quality. Several embodiments provide methods to generate a model to assess image quality.

Abstract: 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.