An ensemble machine-learning model improved sensitivity and specificity for discriminating HCM from athlete's heart compared with conventional echocardiographic parameters (p<0.01).
Observational (n=139)
Does an ensemble machine-learning model using speckle-tracking echocardiographic data improve the discrimination of hypertrophic cardiomyopathy from physiological hypertrophy in athletes compared to conventional echocardiographic parameters?
An ensemble machine-learning model using speckle-tracking echocardiographic data improves the discrimination between hypertrophic cardiomyopathy and physiological athlete's heart compared to conventional echocardiographic parameters.
p-value: p=<0.01
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 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.
Narula et al. (2016) conducted an observational in Hypertrophic cardiomyopathy and physiological hypertrophy (n=139). Ensemble machine-learning model vs. Conventional echocardiographic parameters (E/A ratio, e', and strain) was evaluated on Discrimination of hypertrophic cardiomyopathy from physiological hypertrophy (p=<0.01). An ensemble machine-learning model improved sensitivity and specificity for discriminating HCM from athlete's heart compared with conventional echocardiographic parameters (p<0.01).