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

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Radiology Experimental Pub Date : 2024-07-12 DOI:10.1186/s41747-024-00477-7
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, Pankaj Garg
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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.

Trials registration: ClinicalTrials.gov: NCT05114785.

Relevance statement: 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.

Key points: • 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.

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开发和验证四腔心肌磁共振的人工智能分割。
背景:四腔平面的心脏磁共振(CMR)可全面了解心脏的容积。我们的目标是开发一种使用四腔CMR进行时间分辨分割的人工智能(AI)模型:方法:使用 814 名受试者的回顾性多中心和多供应商数据训练全自动深度学习算法。对 101 名受试者组成的独立队列进行了验证、可重复性和死亡率预测评估:验证组群的平均年龄为 54 岁,男性 66 人(占 65%)。左心和右心参数在自动分析和手动分析之间显示出很强的相关性,ρ分别为0.91-0.98和0.89-0.98,偏差极小。重复性分析中的所有人工智能四腔容积均显示出高度相关性(ρ = 0.99-1.00),且无偏差。与地面实况短轴 cine 分析相比,自动四腔分析低估了左心室和右心室容积。根据系统性偏差,为左心室和右心室四腔分析提出了两个校正因子。应用校正因子后,观察到左心室容积测量的相关性很强,偏差很小。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,手动分析(危险比 (HR) = 0.96,P = 0.003)和人工智能分析(HR = 0.96,P 结论:左房射血分数与死亡有独立关联:全自动四腔 CMR 是可行的、可重复的,并且在现实世界中具有与人工分析相同的预后价值。四腔分割得出的左心室容积与短轴容积评估结果相当:试验注册:ClinicalTrials.gov:NCT05114785.Relevance statement:在CMR中整合全自动人工智能有望彻底改变临床心脏评估,为改善患者护理和预后提供高效、准确和有价值的见解:- 四腔Cine序列仍然是CMR检查中信息量最大的采集之一。- 这套基于深度学习、时间分辨、全自动的四腔容积、功能和形变分析解决方案,可对四腔CT序列的左心室和左心室容积进行分析。- 与地面真实短轴分割相比,四腔分析低估了左心室和左心室容积。- 通过四腔分割纠正左心室和左心室容积偏差,最大限度地减少系统性偏差。
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来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
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