Föllmer Bernhard, Williams Michelle C, Dey Damini, Arbab-Zadeh Armin, Maurovich-Horvat Pál, Volleberg Rick H J A, Rueckert Daniel, Schnabel Julia A, Newby David E, Dweck Marc R, Guagliumi Giulio, Falk Volkmar, Vázquez Mézquita Aldo J, Biavati Federico, Išgum Ivana, Dewey Marc
Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Nat Rev Cardiol. 2024 Jan;21(1):51-64. doi: 10.1038/s41569-023-00900-3. Epub 2023 Jul 18.
Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training, and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries, but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this Roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and non-invasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.
人工智能（AI）很可能会彻底改变医学图像的分析方式，并有潜力改善冠状动脉中易损或高危动脉粥样硬化斑块的识别与分析，推动冠状动脉疾病治疗取得进展。然而，由于心脏和呼吸运动以及心血管结构尺寸较小，冠状动脉斑块分析具有挑战性。此外，冠状动脉成像数据的分析耗时，只有经过专门心血管成像培训的临床医生才能进行，并且在不同阅片者和同一阅片者之间存在相当大的变异性。人工智能有潜力改善对冠状动脉易损斑块图像的评估，但需要进行强有力的开发、测试和验证。将人类专业知识与人工智能相结合，可能有助于对使用CT、MRI、PET、血管内超声和光学相干断层扫描获得的图像进行可靠且有效的解读。在本路线图中，我们回顾了关于人工智能应用于冠状动脉易损斑块成像的现有证据，并提供了由人工智能以及无创和有创冠状动脉成像领域的跨学科专家小组制定的共识建议。我们还概述了人工智能技术在解决偏差、不确定性、可解释性和通用性方面的未来要求，这些对于人工智能的接受及其在处理预期不断增加的冠状动脉成像程序中的临床效用至关重要。
