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Cardiac Valve Event Timing in Echocardiography Using Deep Learning and Triplane Recordings

Abstract

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.

Category

Academic article

Language

English

Author(s)

  • Benjamin Strandli Fermann
  • John Nyberg
  • Espen Remme
  • Jahn Frederik Grue
  • Helén Grue
  • Roger Håland
  • Lasse Løvstakken
  • Håvard Dalen
  • Bjørnar Leangen Grenne
  • Svein Arne Aase
  • Sten Roar Snare
  • Andreas Østvik

Affiliation

  • SINTEF Digital / Health Research
  • University of Oslo
  • St. Olavs Hospital, Trondheim University Hospital
  • Norwegian University of Science and Technology
  • GE Vingmed Ultrasound AS
  • Oslo University Hospital

Year

2024

Published in

IEEE Journal of Biomedical and Health Informatics

ISSN

2168-2194

Volume

28

Issue

5

Page(s)

2759 - 2768

View this publication at Norwegian Research Information Repository