Cardiac valve event timing in echocardiography using deep learning and triplane recordings
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866929278150508544 |
|---|---|
| author | Fermann, Benjamin Strandli Nyberg, John Remme, Espen W. Grue, Jahn Frederik Grue, Helén Håland, Roger Lovstakken, Lasse Dalen, Håvard Grenne, Bjørnar Aase, Svein Arne Snar, Sten Roar Østvik, Andreas |
| author_facet | Fermann, Benjamin Strandli Nyberg, John Remme, Espen W. Grue, Jahn Frederik Grue, Helén Håland, Roger Lovstakken, Lasse Dalen, Håvard Grenne, Bjørnar Aase, Svein Arne Snar, Sten Roar Østvik, Andreas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10156 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cardiac valve event timing in echocardiography using deep learning and triplane recordings Fermann, Benjamin Strandli Nyberg, John Remme, Espen W. Grue, Jahn Frederik Grue, Helén Håland, Roger Lovstakken, Lasse Dalen, Håvard Grenne, Bjørnar Aase, Svein Arne Snar, Sten Roar Østvik, Andreas Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Cardiac valve event timing in echocardiography using deep learning and triplane recordings |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.10156 |