期刊
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
卷 68, 期 21, 页码 2287-2295出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jacc.2016.08.062
关键词
cardiomyopathy; decision; support systems; left ventricular; hypertrophy; speckle-tracking echocardiography
资金
- National Institute of Health: National Institute of Diabetes and Digestive and Kidney Diseases [R01DK098242]
- National Institute of Health: National Cancer Institute [U54CA189201, U54-CA189201-02]
- National Institute of Health: Illuminating the Druggable Genome
- National Institute of Health: Knowledge Management Center - National Institutes of Health Common Fund
- National Institute of Health: National Center for Advancing Translational Sciences [UL1TR000067]
- National Institute of Health: Clinical and Translational Science Award
- Janssen Pharmaceuticals
- GlaxoSmithKline
- AstraZeneca
- Hoffman-La Roche
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. (C) 2016 by the American College of Cardiology Foundation.
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