Sergio Sanchez-Martinez

TL;DR: This work tested the hypothesis that a machine learning algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT).

TL;DR: The analysis of left ventricular long-axis function on exercise by interpretable ML may improve the diagnosis and understanding of HFpEF.

TL;DR: Machine learning of spatiotemporal variations of LV strain rate during rest and exercise could be used to identify patients with HFpEF and to provide an objective basis for diagnostic classification.

TL;DR: A brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of Fetal cardiac remodeling and abnormalities is provided.

TL;DR: The results confirm that characterization of the myocardial functional response to stress in the HFPEF syndrome may be improved by the joint analysis of multiple relevant features.