Farina Juan M, Pereyra Milagros, Mahmoud Ahmed K, Scalia Isabel G, Abbas Mohammed Tiseer, Chao Chieh-Ju, Barry Timothy, Ayoub Chadi, Banerjee Imon, Arsanjani Reza
Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA.
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA.
J Imaging. 2023 Oct 26;9(11):236. doi: 10.3390/jimaging9110236.
Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation.
胸部X线摄影（CXR）是全球最常进行的放射学检查，因为其广泛可得、无创且成本低。CXR诊断心血管疾病、洞察心脏功能以及预测心血管事件的能力常常未得到充分利用，未被清楚理解，且受观察者间和观察者内变异性的影响。因此，通常需要更复杂的检查来评估心血管疾病。考虑到心血管疾病发病率持续上升，找到可及、快速且可重复的检查以帮助诊断这些常见病症至关重要。对人工智能（AI）在心血管诊断成像方面应用的关注度不断提高，这也已应用于CXR，有几篇出版物表明可以通过识别CXR中的特征来训练AI模型以检测心血管病症。已经开发了多种模型来预测死亡率、心血管形态和功能、冠状动脉疾病、心脏瓣膜病、主动脉疾病、心律失常、肺动脉高压和心力衰竭。现有证据表明，将基于AI的工具应用于CXR以诊断心血管病症和进行预后评估有可能改变临床护理。未来，经AI分析的CXR可作为一种辅助的、易于应用的技术，用于改善心血管疾病的诊断和风险分层。这些进展可能有助于更好地针对更高级的检查，这在某些情况下可能会减轻检查负担，同时更好地识别将从早期、专门和全面的心血管评估中受益的高风险患者。
