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

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Abstract

Background

Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation.

Objectives

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).

Methods

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.

Results

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.

Conclusions

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.

Key Words

cardiomyopathy
decision support systems
left ventricular hypertrophy
speckle-tracking echocardiography

Abbreviations and Acronyms

2D
2-dimensional
ATH
athletes
E/A
early-to-late diastolic transmitral velocity ratio
HCM
hypertrophic cardiomyopathy
IG
information gain
LS
longitudinal strain
LV
left ventricular
STE
speckle-tracking echocardiography

Cited by (0)

The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. Drs. Shameer and Dudley have received the grants from National Institutes of Health: National Institute of Diabetes and Digestive and Kidney Diseases (R01DK098242); National Cancer Institute (U54CA189201); Illuminating the Druggable Genome; Knowledge Management Center sponsored by National Institutes of Health Common Fund; National Cancer Institute (U54-CA189201-02); National Center for Advancing Translational Sciences (UL1TR000067); and Clinical and Translational Science Award. Dr. Dudley has received consulting fees or honoraria from Janssen Pharmaceuticals, GlaxoSmithKline, AstraZeneca, and Hoffman-La Roche; is a scientific advisor to LAM Therapeutics; and holds equity in NuMedii Inc., Ayasdi Inc., and Ontomics, Inc. Dr. Sengupta is a consultant for TeleHealthRobotics, Heart Test Labs, and Hitachi-Aloka Ltd. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Mr. Narula and Dr. Shameer contributed equally to this work. Presented at the American Society of Echocardiography Annual Scientific Sessions 2015 for Arthur Weyman Young Investigator Award. Mr. Narula was declared as the winner of the competition. P.K. Shah, MD, served as Guest Editor-in-Chief for this paper.

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