Langs G, Röhrich S, Hofmanninger J, Prayer F, Pan J, Herold C, Prosch H
Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
Radiologe. 2018 Nov;58(Suppl 1):1-6. doi: 10.1007/s00117-018-0407-3.
Machine learning is rapidly gaining importance in radiology. It allows for the exploitation of patterns in imaging data and in patient records for a more accurate and precise quantification, diagnosis, and prognosis. Here, we outline the basics of machine learning relevant for radiology, and review the current state of the art, the limitations, and the challenges faced as these techniques become an important building block of precision medicine. Furthermore, we discuss the roles machine learning can play in clinical routine and research and predict how it might change the field of radiology.
机器学习在放射学领域正迅速变得愈发重要。它能够利用成像数据和患者记录中的模式，以实现更准确、精确的量化、诊断和预后评估。在此，我们概述与放射学相关的机器学习基础知识，并回顾当前的技术水平、局限性以及随着这些技术成为精准医学的重要组成部分所面临的挑战。此外，我们还将讨论机器学习在临床常规和研究中可以发挥的作用，并预测它可能如何改变放射学领域。
