本文由小咖机器人翻译整理

期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02971-2

摘要内容如下：

心脏磁共振成像（CMR）是评估心脏功能的金标准，在诊断心血管疾病（CVD）中起着至关重要的作用。然而，CMR解释的沉重资源负担限制了其广泛应用。在这里，为了应对这一挑战，我们开发并验证了计算机CMR解释，用于筛查和诊断9719名患者的11种心血管疾病。我们提出了一个两阶段的模式，包括非侵入性的基于电影的CVD筛查，然后是基于电影和晚期钆增强的诊断。筛选和诊断模型在内部和外部数据集中都实现了高性能（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学家，证明了人工智能CMR检测先前未识别的CMR特征的能力。这项概念验证研究有可能大大提高CMR解释的效率和可扩展性，从而改善CVD筛查和诊断。

英文原文如下：

Abstracts

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.

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