Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance

Abstract

Background Cardiac magnetic resonance (CMR) in the four-chamber plane ofers comprehensive insight into the volumetrics of the heart. We aimed to develop an artifcial intelligence (AI) model of time-resolved segmenta‑ tion 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 parame‑ ters 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 correla‑ tion (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.