Department of Medicine, Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK.
Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK.
Medicina (Kaunas). 2022 Aug 12;58(8):1087. doi: 10.3390/medicina58081087.
Background and Objectives: Interest in artificial intelligence (AI) for outcome prediction has grown substantially in recent years. However, the prognostic role of AI using advanced cardiac magnetic resonance imaging (CMR) remains unclear. This systematic review assesses the existing literature on AI in CMR to predict outcomes in patients with cardiovascular disease. Materials and Methods: Medline and Embase were searched for studies published up to November 2021. Any study assessing outcome prediction using AI in CMR in patients with cardiovascular disease was eligible for inclusion. All studies were assessed for compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: A total of 5 studies were included, with a total of 3679 patients, with 225 deaths and 265 major adverse cardiovascular events. Three methods demonstrated high prognostic accuracy: (1) three-dimensional motion assessment model in pulmonary hypertension (hazard ratio (HR) 2.74, 95%CI 1.73−4.34, p < 0.001), (2) automated perfusion quantification in patients with coronary artery disease (HR 2.14, 95%CI 1.58−2.90, p < 0.001), and (3) automated volumetric, functional, and area assessment in patients with myocardial infarction (HR 0.94, 95%CI 0.92−0.96, p < 0.001). Conclusion: There is emerging evidence of the prognostic role of AI in predicting outcomes for three-dimensional motion assessment in pulmonary hypertension, ischaemia assessment by automated perfusion quantification, and automated functional assessment in myocardial infarction.
近年来，人们对人工智能（AI）在预后预测方面的兴趣大幅增加。然而，利用先进的心脏磁共振成像（CMR）进行 AI 预测的预后作用尚不清楚。本系统评价评估了 AI 在 CMR 中预测心血管疾病患者结局的现有文献。
检索了截至 2021 年 11 月发表的 Medline 和 Embase 中的研究。任何使用 CMR 中的 AI 评估心血管疾病患者结局预测的研究均符合纳入标准。所有研究均评估了其对医学影像中人工智能检查表（CLAIM）的符合情况。
共纳入 5 项研究，共计 3679 例患者，其中 225 例死亡，265 例发生主要不良心血管事件。有 3 种方法显示出较高的预后准确性：（1）肺动脉高压的三维运动评估模型（风险比（HR）2.74，95%置信区间 1.73−4.34，p<0.001），（2）冠心病患者的自动灌注定量（HR 2.14，95%置信区间 1.58−2.90，p<0.001），以及（3）心肌梗死患者的自动容积、功能和面积评估（HR 0.94，95%置信区间 0.92−0.96，p<0.001）。
有证据表明 AI 在预测肺动脉高压的三维运动评估、自动灌注定量的缺血评估以及心肌梗死的自动功能评估中的预后作用。
