title: Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.
abstract: Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation.
  
  
  
  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).
  
  
  
  Expert-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-fold cross-validation.
  
  
  
  Feature 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 < 0.01), average early diastolic tissue velocity (e') (p < 0.01), and strain (p = 0.04). Because ATH were younger, adjusted analysis was undertaken in younger HCM patients and compared with ATH with left ventricular wall thickness >13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e', and strain.
  
  
  
  Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning-based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience.
authors.affiliation: Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
authors.forename: Sukrit
authors.initials: S
authors.lastname: Narula
authors.affiliation: Institute of Next Generation Healthcare, Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, New York.
authors.forename: Khader
authors.initials: K
authors.lastname: Shameer
authors.affiliation: Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Internal Medicine, Medical Division, National Research Center, Cairo, Egypt.
authors.forename: Alaa Mabrouk
authors.initials: AM
authors.lastname: Salem Omar
authors.affiliation: Institute of Next Generation Healthcare, Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, New York.
authors.forename: Joel T
authors.initials: JT
authors.lastname: Dudley
authors.affiliation: Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York. Electronic address: partho.sengupta@mountsinai.org.
authors.forename: Partho P
authors.initials: PP
authors.lastname: Sengupta
mesh_terms: D000328:Adult; D000465:Algorithms; D056352:Athletes; D002312:Cardiomyopathy, Hypertrophic / Q000175:diagnosis* / Q000503:physiopathology; D004452:Echocardiography / Q000379:methods*; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D008432:Mathematical Computing; D008875:Middle Aged; D016277:Ventricular Function, Left / Q000502:physiology*
keywords: cardiomyopathy; decision support systems; left ventricular hypertrophy; speckle-tracking echocardiography