Clinical assessment of an AI tool for measuring biventricular parameters on cardiac MR.

Autor:	Salehi M; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom., Maiter A; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.; Department of Cardiovascular Disease, NIHR Sheffield Biomedical Research Centre, Sheffield, United Kingdom., Strickland S; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom., Aldabbagh Z; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom., Karunasaagarar K; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom., Thomas R; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom., Lopez-Dee T; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom., Capener D; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom., Dwivedi K; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom., Sharkey M; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom., Metherall P; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom., van der Geest R; Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands., Alabed S; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.; Department of Cardiovascular Disease, NIHR Sheffield Biomedical Research Centre, Sheffield, United Kingdom., Swift AJ; Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.; Department of Cardiovascular Disease, NIHR Sheffield Biomedical Research Centre, Sheffield, United Kingdom.
Jazyk:	angličtina
Zdroj:	Frontiers in cardiovascular medicine [Front Cardiovasc Med] 2024 Feb 05; Vol. 11, pp. 1279298. Date of Electronic Publication: 2024 Feb 05 (Print Publication: 2024).
DOI:	10.3389/fcvm.2024.1279298
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (© 2024 Salehi, Maiter, Strickland, Aldabbagh, Karunasaagarar, Thomas, Lopez-Dee, Capener, Dwivedi, Sharkey, Metherall, van der Geest, Alabed and Swift.)
Databáze:	MEDLINE
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