医学
肺癌筛查
肺癌
工作流程
计算机断层摄影术
人工智能
放射科
肺
医学物理学
机器学习
病理
内科学
计算机科学
数据库
作者
Scott Adams,Peter G. Mikhael,Jeremy Wohlwend,Regina Barzilay,Lecia V. Sequist,Florian J. Fintelmann
出处
期刊:Thoracic Surgery Clinics
日期:2023-05-12
卷期号:33 (4): 401-409
被引量:31
标识
DOI:10.1016/j.thorsurg.2023.03.001
摘要
Recent advances in artificial intelligence and machine learning (AI/ML) hold substantial promise to address some of the current challenges in lung cancer screening and improve health equity. This article reviews the status and future directions of AI/ML tools in the lung cancer screening workflow, focusing on determining screening eligibility, radiation dose reduction and image denoising for low-dose chest computed tomography (CT), lung nodule detection, lung nodule classification, and determining optimal screening intervals. AI/ML tools can assess for chronic diseases on CT, which creates opportunities to improve population health through opportunistic screening.
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