Jeremy A Slivnick, Nils T Gessert, Juan I Cotella, Lucas Oliveira, Nicola Pezzotti, Parastou Eslami, Ali Sadeghi, Simon Wehle, David Prabhu, Irina Waechter-Stehle, Ashish M Chaudhari, Teodora Szasz, Linda Lee, Marie Altenburg, Giancarlo Saldana, Michael Randazzo, Jeanne M DeCara, Karima Addetia, Victor Mor-Avi, Roberto M Lang
BACKGROUND: Although regional wall motion abnormality (RWMA) detection is foundational to transthoracic echocardiography (TTE), current methods are prone to inter-observer variability. We aimed to develop a deep learning (DL) model for RWMA assessment and compare it to expert and novice readers. METHODS: We used 15,746 TTE studies-including 25,529 apical videos-which were split into training, validation, and test datasets. A convolutional neural network was trained and validated using apical 2-, 3-, and 4-chamber videos to predict the presence of RWMA in 7 regions defined by coronary perfusion territories, using the ground truth derived from clinical TTE reports...
March 29, 2024: Journal of the American Society of Echocardiography