Echocardiographic View Classification with Integrated Out-of-Distribution Detection for Enhanced Automatic Echocardiographic Analysis
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In the rapidly evolving field of automatic echocardiographic analysis and interpretation, automatic view classification is a critical yet challenging task, owing to the inherent complexity and variability of echocardiographic data. This study presents ECHOcardiography VIew Classification with Out-of-Distribution dEtection (ECHO-VICODE), a novel deep learning-based framework that effectively addresses this challenge by training to classify 31 classes, surpassing previous studies and demonstrating its capacity to handle a wide range of echocardiographic views. Furthermore, ECHO-VICODE incorporates an integrated out-of-distribution (OOD) detection function, leveraging the relative Mahalanobis distance to effectively identify 'near-OOD' instances commonly encountered in echocardiographic data. Through extensive experimentation, we demonstrated the outstanding performance of ECHO-VICODE in terms of view classification and OOD detection, significantly reducing the potential for errors in echocardiographic analyses. This pioneering study significantly advances the domain of automated echocardiography analysis and exhibits promising prospects for substantial applications in extensive clinical research and practice.
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