Department of Radiology, University of Florida, Gainesville, FL.
Department of Radiology, University of Florida, Gainesville, FL.
Semin Roentgenol. 2023 Apr;58(2):184-195. doi: 10.1053/j.ro.2023.02.001. Epub 2023 Mar 5.
Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.
人工智能算法可以通过吸收来自大型数据集的信息来学习，以便破译复杂的关联、识别以前未发现的病理生理状态和构建预测模型。人工智能在包括医疗保健在内的各个行业中的兴趣和应用都在增加。因此，用于医学成像的各种应用的研究文章和行业参与者的数量呈指数级增长，这对于放射科医生来说很难进行导航。在胸部成像中，正在评估多种应用程序，包括胸部 X 射线和计算机断层扫描，包括肺结节评估和癌症成像、定量弥漫性肺疾病和心脏成像等应用。本综述旨在概述当前的临床 AI 模型，重点介绍人工智能在心胸成像中的最常见临床应用。
