Khosravi Bardia, Rouzrokh Pouria, Faghani Shahriar, Moassefi Mana, Vahdati Sanaz, Mahmoudi Elham, Chalian Hamid, Erickson Bradley J
Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA.
Diagnostics (Basel). 2022 Oct 17;12(10):2512. doi: 10.3390/diagnostics12102512.
Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.
机器学习（ML）和深度学习（DL）算法是一组建模算法的一部分，这些算法基于训练过程掌握数据中的隐藏模式，使其能够从输入数据中提取复杂信息。在过去十年中，这些算法越来越多地用于图像处理，特别是在医学领域。心胸成像领域是ML/DL研究的早期采用者之一，而新冠疫情使更多研究聚焦于ML/DL在心胸成像中的可行性和应用。在本范围综述中，我们系统检索了关于心胸成像的同行评审医学文献，并定量提取关键数据元素，以便全面了解ML/DL在快速发展的心胸成像领域中的应用情况。在本报告中，我们提供了关于ML/DL不同应用的见解以及与这一特定研究领域相关的一些细微差别。最后，我们就研究人员如何使他们的研究不仅仅是概念验证并朝着临床应用方向发展提供了一般性建议。
