Department of Cardiovascular and Metabolic Health, Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK.
Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk, UK.
Eur Radiol Exp. 2024 Jul 12;8(1):77. doi: 10.1186/s41747-024-00477-7.
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.
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.
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).
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.
ClinicalTrials.gov: NCT05114785.
Integrating fully automated AI in CMR promises to revolutionise clinical cardiac assessment, offering efficient, accurate, and prognostically valuable insights for improved patient care and outcomes.
• Four-chamber cine sequences remain one of the most informative acquisitions in CMR examination. • This deep learning-based, time-resolved, fully automated four-chamber volumetric, functional, and deformation analysis solution. • LV and RV were underestimated by four-chamber analysis compared to ground truth short-axis segmentation. • Correction bias for both LV and RV volumes by four-chamber segmentation, minimises the systematic bias.
心脏磁共振（CMR）在四腔心平面提供了对心脏容积的全面了解。我们旨在使用四腔心电影开发一种基于人工智能（AI）的时间分辨分割模型。
使用来自 814 名受试者的回顾性多中心和多供应商数据，对完全自动化的深度学习算法进行了训练。在由 101 名受试者组成的独立队列中评估验证、可重复性和死亡率预测。
验证队列的平均年龄为 54 岁，66 名（65%）为男性。自动分析和手动分析之间的左心和右心参数具有很强的相关性，ρ 值分别为 0.91-0.98 和 0.89-0.98，偏差最小。在重复性分析中，所有 AI 四腔心容积均具有高相关性（ρ=0.99-1.00），且无偏差。与基于短轴电影分析的地面真实值相比，自动四腔心分析低估了左心室（LV）和右心室（RV）的容积。基于系统偏差，提出了用于 LV 和 RV 四腔心分析的两个校正因子。应用校正因子后，LV 容积具有很强的相关性和最小的偏差。在平均 6.75 年的随访期间，16 名患者死亡。在逐步多变量分析中，左心房射血分数在手动分析（风险比（HR）=0.96，p=0.003）和 AI 分析（HR=0.96，p<0.001）中均与死亡具有独立关联。
完全自动化的四腔心 CMR 是可行的、可重复的，并且与手动分析具有相同的实际预后价值。四腔心节段的 LV 容积与短轴容积评估相当。
ClinicalTrials.gov：NCT05114785。
将完全自动化的人工智能整合到 CMR 中有望彻底改变临床心脏评估，为改善患者护理和结果提供高效、准确和具有预后价值的见解。
四腔心电影序列仍然是 CMR 检查中最具信息量的采集之一。
这是一种基于深度学习的、时间分辨的、完全自动化的四腔心容积、功能和变形分析解决方案。
与基于短轴分割的地面真实值相比，四腔心分析低估了 LV 和 RV。
通过四腔心分割，LV 和 RV 容积的校正偏倚最小化了系统偏倚。
