Multibeat echocardiographic phase detection using deep neural networks

Lane, Elisabeth S., Azarmehr, Neda, Jevsikov, Jevgeni, Howard, James P., Shun-shin, Matthew J., Cole, Graham D., Francis, Darrel P. and Zolgharni, Massoud ORCID: (2021) Multibeat echocardiographic phase detection using deep neural networks. Computers in Biology and Medicine, 133. p. 104373. ISSN 0010-4825

[thumbnail of Beth-accepted version.pdf]
Beth-accepted version.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB) | Preview


Accurate identification of end-diastolic and end-systolic frames in echocardiographic cine loops is important, yet challenging, for human experts. Manual frame selection is subject to uncertainty, affecting crucial clinical measurements, such as myocardial strain. Therefore, the ability to automatically detect frames of interest is highly desirable.

We have developed deep neural networks, trained and tested on multi-centre patient data, for the accurate identification of end-diastolic and end-systolic frames in apical four-chamber 2D multibeat cine loop recordings of arbitrary length. Seven experienced cardiologist experts independently labelled the frames of interest, thereby providing infallible annotations, allowing for observer variability measurements.

When compared with the ground-truth, our model shows an average frame difference of −0.09 ± 1.10 and 0.11 ± 1.29 frames for end-diastolic and end-systolic frames, respectively. When applied to patient datasets from a different clinical site, to which the model was blind during its development, average frame differences of −1.34 ± 3.27 and −0.31 ± 3.37 frames were obtained for both frames of interest. All detection errors fall within the range of inter-observer variability: [-0.87, −5.51]±[2.29, 4.26] and [-0.97, −3.46]±[3.67, 4.68] for ED and ES events, respectively.

The proposed automated model can identify multiple end-systolic and end-diastolic frames in echocardiographic videos of arbitrary length with performance indistinguishable from that of human experts, but with significantly shorter processing time.

Item Type: Article
Identifier: 10.1016/j.compbiomed.2021.104373
Keywords: Echocardiography, Cardiac imaging, Deep learning, Phase detection
Subjects: Computing > Intelligent systems
Related URLs:
Depositing User: Massoud Zolgharni
Date Deposited: 13 Apr 2021 10:03
Last Modified: 06 Feb 2024 16:05


Downloads per month over past year

Actions (login required)

View Item View Item