Cardiac valve event timing in echocardiography using deep learning and
  triplane recordings
Benjamin Strandli Fermann John Nyberg Espen W. Remme Jahn Frederik Grue Helén Grue Roger Håland Lasse Lovstakken Håvard Dalen Bjørnar Grenne Svein Arne Aase Sten Roar Snar Andreas Østvik
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. Our method demonstrates improved performance detecting six
different events, including valve events conventionally associated with ED and
ES. Of all events, we achieve an average absolute frame difference (aFD) of
maximum 1.4 frames (29 ms) for start of diastasis, down to 0.6 frames (12 ms)
for mitral valve opening when performing a ten-fold cross-validation with test
splits on triplane data from 240 patients. On an external independent test
consisting of apical long-axis data from 180 other patients, the worst
performing event detection had an aFD of 1.8 (30 ms). The proposed approach has
the potential to significantly impact clinical practice by enabling more
accurate, rapid and comprehensive event detection, leading to improved clinical
measurements.