Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University.
Magn Reson Med Sci. 2023 Apr 1;22(2):147-156. doi: 10.2463/mrms.rev.2022-0102. Epub 2023 Jan 26.
The application of machine learning (ML) and deep learning (DL) in radiology has expanded exponentially. In recent years, an extremely large number of studies have reported about the hepatobiliary domain. Its applications range from differential diagnosis to the diagnosis of tumor invasion and prediction of treatment response and prognosis. Moreover, it has been utilized to improve the image quality of DL reconstruction. However, most clinicians are not familiar with ML and DL, and previous studies about these concepts are relatively challenging to understand. In this review article, we aimed to explain the concepts behind ML and DL and to summarize recent achievements in their use in the hepatobiliary region.
机器学习 (ML) 和深度学习 (DL) 在放射学中的应用呈指数级增长。近年来,大量研究报告了关于肝胆领域的应用。其应用范围从鉴别诊断到肿瘤侵袭的诊断,以及治疗反应和预后的预测。此外,它还被用于提高 DL 重建的图像质量。然而,大多数临床医生对 ML 和 DL 并不熟悉,以前关于这些概念的研究也相对难以理解。在这篇综述文章中,我们旨在解释 ML 和 DL 的概念,并总结它们在肝胆区域应用的最新成果。