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.

翻译：在自动超声心动图分析与解读这一快速发展的领域中，由于超声心动图数据固有的复杂性和变异性，自动视图分类是一项关键但具有挑战性的任务。本研究提出了ECHOcardiography VIew Classification with Out-of-Distribution dEtection（ECHO-VICODE），一种基于深度学习的新型框架，通过训练实现对31类视图的分类，超越了先前的研究，展示了其处理广泛超声心动图视图的能力。此外，ECHO-VICODE集成了分布外（OOD）检测功能，利用相对马氏距离有效识别超声心动图数据中常见的“近OOD”实例。通过广泛的实验，我们证明了ECHO-VICODE在视图分类和OOD检测方面的卓越性能，显著降低了超声心动图分析中潜在的误差风险。这项开创性研究显著推进了自动超声心动图分析领域，并在广泛的临床研究和实践中展现出巨大的应用前景。