Automated mitral inflow Doppler peak velocity measurement using deep learning
Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:171 |
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| Enthalten in: |
Computers in biology and medicine - 171(2024) vom: 21. März, Seite 108192 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jevsikov, Jevgeni [VerfasserIn] |
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| Links: |
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| Themen: |
Automated analysis |
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| Anmerkungen: |
Date Completed 21.03.2024 Date Revised 21.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2024.108192 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 520 | |a Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Doppler echocardiography | |
| 650 | 4 | |a Mitral inflow | |
| 700 | 1 | |a Ng, Tiffany |e verfasserin |4 aut | |
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| 700 | 1 | |a Naidoo, Preshen |e verfasserin |4 aut | |
| 700 | 1 | |a Fernandes, Patricia |e verfasserin |4 aut | |
| 700 | 1 | |a Sehmi, Joban S |e verfasserin |4 aut | |
| 700 | 1 | |a Alzetani, Maysaa |e verfasserin |4 aut | |
| 700 | 1 | |a Demetrescu, Camelia D |e verfasserin |4 aut | |
| 700 | 1 | |a Azarmehr, Neda |e verfasserin |4 aut | |
| 700 | 1 | |a Serej, Nasim Dadashi |e verfasserin |4 aut | |
| 700 | 1 | |a Stowell, Catherine C |e verfasserin |4 aut | |
| 700 | 1 | |a Shun-Shin, Matthew J |e verfasserin |4 aut | |
| 700 | 1 | |a Francis, Darrel P |e verfasserin |4 aut | |
| 700 | 1 | |a Zolgharni, Massoud |e verfasserin |4 aut | |
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