Benjamin Strandli Fermann, John Nyberg, Espen W Remme, Jahn Frederik Grue, Helen Grue, Roger Haland, Lasse Lovstakken, Havard Dalen, Bjornar Grenne, Svein Arne Aase, Sten Roar Snare, Andreas Ostvik
Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely on timing from different cardiac cycles. Recent methods have applied deep learning to cardiac timing, but they have mainly been restricted to only detecting two key time points, namely end-diastole (ED) and end-systole (ES). In this work, we propose a deep learning approach that leverages triplane recordings to enhance detection of valve events in echocardiography...
March 5, 2024: IEEE Journal of Biomedical and Health Informatics