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Assadi, Hosamadin 
ORCID: https://orcid.org/0000-0002-6143-8095, Alabed, Samer, Li, Rui, Matthews, Gareth ORCID: https://orcid.org/0000-0001-8353-4806, Karunasaagarar, Kavita, Kasmai, Bahman, Nair, Sunil, Mehmood, Zia, Grafton-Clarke, Ciaran ORCID: https://orcid.org/0000-0002-8537-0806, Swoboda, Peter P., Swift, Andrew J., Greenwood, John P., Vassiliou, Vassilios S. ORCID: https://orcid.org/0000-0002-4005-7752, Plein, Sven, van der Geest, Rob J. and Garg, Pankaj ORCID: https://orcid.org/0000-0002-5483-169X
(2024)
Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance.
European Radiology Experimental, 8.
ISSN 2509-9280
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Abstract
Background: Cardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine. Methods: A fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects. Results: The mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91−0.98 and 0.89−0.98, respectively, with minimal bias. All AI four-chamber volumetrics in repeatability analysis demonstrated high correlation (ρ = 0.99−1.00) and no bias. Automated four-chamber analysis underestimated both left ventricular (LV) and right ventricular (RV) volumes compared to ground-truth short-axis cine analysis. Two correction factors for LV and RV four-chamber analysis were proposed based on systematic bias. After applying the correction factors, a strong correlation and minimal bias for LV volumetrics were observed. During a mean follow-up period of 6.75 years, 16 patients died. On stepwise multivariable analysis, left atrial ejection fraction demonstrated an independent association with death in both manual (hazard ratio (HR) = 0.96, p = 0.003) and AI analyses (HR = 0.96, p < 0.001). Conclusion: Fully automated four-chamber CMR is feasible, reproducible, and has the same real-world prognostic value as manual analysis. LV volumes by four-chamber segmentation were comparable to short-axis volumetric assessment.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence,deep learning,heart diseases,magnetic resonance imaging (cine),prognosis,radiology nuclear medicine and imaging ,/dk/atira/pure/subjectarea/asjc/2700/2741 |
| Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School |
| UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Cardiovascular and Metabolic Health Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 12 Jul 2024 11:31 |
| Last Modified: | 30 Sep 2024 00:25 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/95895 |
| DOI: | 10.1186/s41747-024-00477-7 |
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