1. Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance
- Author
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Hosamadin Assadi, Samer Alabed, Rui Li, Gareth Matthews, Kavita Karunasaagarar, Bahman Kasmai, Sunil Nair, Zia Mehmood, Ciaran Grafton-Clarke, Peter P. Swoboda, Andrew J. Swift, John P. Greenwood, Vassilios S. Vassiliou, Sven Plein, Rob J. van der Geest, and Pankaj Garg
- Subjects
Artificial intelligence, Deep learning, Heart diseases, Magnetic resonance imaging (cine), Prognosis ,Medical physics. Medical radiology. Nuclear medicine ,R895-920 - Abstract
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
- Published
- 2024
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