Clinical assessment of an AI tool for measuring biventricular parameters on cardiac MR
© 2024 Salehi, Maiter, Strickland, Aldabbagh, Karunasaagarar, Thomas, Lopez-Dee, Capener, Dwivedi, Sharkey, Metherall, van der Geest, Alabed and Swift..
Introduction: Cardiac magnetic resonance (CMR) is of diagnostic and prognostic value in a range of cardiopulmonary conditions. Current methods for evaluating CMR studies are laborious and time-consuming, contributing to delays for patients. As the demand for CMR increases, there is a growing need to automate this process. The application of artificial intelligence (AI) to CMR is promising, but the evaluation of these tools in clinical practice has been limited. This study assessed the clinical viability of an automatic tool for measuring cardiac volumes on CMR.
Methods: Consecutive patients who underwent CMR for any indication between January 2022 and October 2022 at a single tertiary centre were included prospectively. For each case, short-axis CMR images were segmented by the AI tool and manually to yield volume, mass and ejection fraction measurements for both ventricles. Automated and manual measurements were compared for agreement and the quality of the automated contours was assessed visually by cardiac radiologists.
Results: 462 CMR studies were included. No statistically significant difference was demonstrated between any automated and manual measurements (p > 0.05; independent T-test). Intraclass correlation coefficient and Bland-Altman analysis showed excellent agreement across all metrics (ICC > 0.85). The automated contours were evaluated visually in 251 cases, with agreement or minor disagreement in 229 cases (91.2%) and failed segmentation in only a single case (0.4%). The AI tool was able to provide automated contours in under 90 s.
Conclusions: Automated segmentation of both ventricles on CMR by an automatic tool shows excellent agreement with manual segmentation performed by CMR experts in a retrospective real-world clinical cohort. Implementation of the tool could improve the efficiency of CMR reporting and reduce delays between imaging and diagnosis.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Frontiers in cardiovascular medicine - 11(2024) vom: 02., Seite 1279298 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Salehi, Mahan [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Revised 21.02.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fcvm.2024.1279298 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368650537 |
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| 520 | |a © 2024 Salehi, Maiter, Strickland, Aldabbagh, Karunasaagarar, Thomas, Lopez-Dee, Capener, Dwivedi, Sharkey, Metherall, van der Geest, Alabed and Swift. | ||
| 520 | |a Introduction: Cardiac magnetic resonance (CMR) is of diagnostic and prognostic value in a range of cardiopulmonary conditions. Current methods for evaluating CMR studies are laborious and time-consuming, contributing to delays for patients. As the demand for CMR increases, there is a growing need to automate this process. The application of artificial intelligence (AI) to CMR is promising, but the evaluation of these tools in clinical practice has been limited. This study assessed the clinical viability of an automatic tool for measuring cardiac volumes on CMR | ||
| 520 | |a Methods: Consecutive patients who underwent CMR for any indication between January 2022 and October 2022 at a single tertiary centre were included prospectively. For each case, short-axis CMR images were segmented by the AI tool and manually to yield volume, mass and ejection fraction measurements for both ventricles. Automated and manual measurements were compared for agreement and the quality of the automated contours was assessed visually by cardiac radiologists | ||
| 520 | |a Results: 462 CMR studies were included. No statistically significant difference was demonstrated between any automated and manual measurements (p > 0.05; independent T-test). Intraclass correlation coefficient and Bland-Altman analysis showed excellent agreement across all metrics (ICC > 0.85). The automated contours were evaluated visually in 251 cases, with agreement or minor disagreement in 229 cases (91.2%) and failed segmentation in only a single case (0.4%). The AI tool was able to provide automated contours in under 90 s | ||
| 520 | |a Conclusions: Automated segmentation of both ventricles on CMR by an automatic tool shows excellent agreement with manual segmentation performed by CMR experts in a retrospective real-world clinical cohort. Implementation of the tool could improve the efficiency of CMR reporting and reduce delays between imaging and diagnosis | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a cardiac | |
| 650 | 4 | |a magnetic resonance imaging | |
| 650 | 4 | |a segmentation | |
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| 700 | 1 | |a Maiter, Ahmed |e verfasserin |4 aut | |
| 700 | 1 | |a Strickland, Scarlett |e verfasserin |4 aut | |
| 700 | 1 | |a Aldabbagh, Ziad |e verfasserin |4 aut | |
| 700 | 1 | |a Karunasaagarar, Kavita |e verfasserin |4 aut | |
| 700 | 1 | |a Thomas, Richard |e verfasserin |4 aut | |
| 700 | 1 | |a Lopez-Dee, Tristan |e verfasserin |4 aut | |
| 700 | 1 | |a Capener, Dave |e verfasserin |4 aut | |
| 700 | 1 | |a Dwivedi, Krit |e verfasserin |4 aut | |
| 700 | 1 | |a Sharkey, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Metherall, Pete |e verfasserin |4 aut | |
| 700 | 1 | |a van der Geest, Rob |e verfasserin |4 aut | |
| 700 | 1 | |a Alabed, Samer |e verfasserin |4 aut | |
| 700 | 1 | |a Swift, Andrew J |e verfasserin |4 aut | |
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