Automated mitral inflow Doppler peak velocity measurement using deep learning.

Autor: Jevsikov J; School of Computing and Engineering, University of West London, United Kingdom; National Heart and Lung Institute, Imperial College London, United Kingdom. Electronic address: Jevgeni.Jevsikov@uwl.ac.uk., Ng T; National Heart and Lung Institute, Imperial College London, United Kingdom., Lane ES; School of Computing and Engineering, University of West London, United Kingdom., Alajrami E; School of Computing and Engineering, University of West London, United Kingdom., Naidoo P; School of Computing and Engineering, University of West London, United Kingdom., Fernandes P; School of Computing and Engineering, University of West London, United Kingdom., Sehmi JS; West Hertfordshire Hospitals NHS Trust, Wafford, United Kingdom., Alzetani M; Luton & Dunstable University Hospital, Bedfordshire, United Kingdom., Demetrescu CD; Luton & Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom., Azarmehr N; School of Computing and Engineering, University of West London, United Kingdom., Serej ND; School of Computing and Engineering, University of West London, United Kingdom., Stowell CC; National Heart and Lung Institute, Imperial College London, United Kingdom., Shun-Shin MJ; National Heart and Lung Institute, Imperial College London, United Kingdom., Francis DP; National Heart and Lung Institute, Imperial College London, United Kingdom., Zolgharni M; School of Computing and Engineering, University of West London, United Kingdom; National Heart and Lung Institute, Imperial College London, United Kingdom.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Mar; Vol. 171, pp. 108192. Date of Electronic Publication: 2024 Feb 23.
DOI: 10.1016/j.compbiomed.2024.108192
Abstrakt: Doppler echocardiography is a widely utilised non-invasive imaging modality for assessing the functionality of heart valves, including the mitral valve. Manual assessments of Doppler traces by clinicians introduce variability, prompting the need for automated solutions. This study introduces an innovative deep learning model for automated detection of peak velocity measurements from mitral inflow Doppler images, independent from Electrocardiogram information. A dataset of Doppler images annotated by multiple expert cardiologists was established, serving as a robust benchmark. The model leverages heatmap regression networks, achieving 96% detection accuracy. The model discrepancy with the expert consensus falls comfortably within the range of inter- and intra-observer variability in measuring Doppler peak velocities. The dataset and models are open-source, fostering further research and clinical application.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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