Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.

心脏磁共振成像（CMR）是心脏功能评估的金标准，在诊断心血管疾病（CVD）中发挥着至关重要的作用。然而，其广泛应用受到 CMR 解释资源负担沉重的限制。为了应对这一挑战，我们开发并验证了计算机化 CMR 解释，用于对 9,719 名患者的 11 种 CVD 进行筛查和诊断。我们提出了一个两阶段范例，包括基于无创电影的 CVD 筛查，然后是基于电影和晚期钆增强的诊断。筛查和诊断模型在内部和外部数据集中均实现了高性能（曲线下面积分别为 0.988 ± 0.3% 和 0.991 ± 0.0%）。此外，该诊断模型在诊断肺动脉高压方面优于心脏病专家，证明了人工智能支持的 CMR 能够检测以前未识别的 CMR 特征。这项概念验证研究有可能大幅提高 CMR 解释的效率和可扩展性，从而改善 CVD 筛查和诊断。