Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography

This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH).MethodsExpert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients  were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K-fol d cross-validation.ResultsFeature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG  = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p 
Source: Journal of the American College of Cardiology - Category: Cardiology Source Type: research