Deep Learning Model for Video-Classification of Echocardiography Images.

Timely and accurate diagnosis of severe Aortic Stenosis (AS) is crucial to prevent severe clinical implications. The most commonly used parameter for diagnostic purposes is the mean transvalvular pressure gradient, measured by echocardio-graphy (>= 40 mmHg). However, its use for detecting severe AS has several limitations, including technical, pathophysiological, and clinical reasons. This study aimed to develop a Deep Learning (DL) model for identifying severe AS using ColorDoppler Echocardiography video data. The new DL model used is called ViViT (Video Vision Transformers). To limit the overfitting problem, the data augmentation technique was applied during the training phase. The model achieved an accuracy of 87% in classifying patients with severe AS compared to healthy subjects in the testing group. Future efforts will focus on enhancing model accuracy, increasing the initial dataset, and refining the classification process by implementing multi-classification of AS with varying degrees of severity.