Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging

被引：7

作者：

Nakaura, Takeshi ^{[1
,2
]}

Kobayashi, Naoki ^{[1
]}

Yoshida, Naofumi ^{[1
]}

Shiraishi, Kaori ^{[1
]}

Uetani, Hiroyuki ^{[1
]}

Nagayama, Yasunori ^{[1
]}

Kidoh, Masafumi ^{[1
]}

Hirai, Toshinori ^{[1
]}

机构：

[1] Kumamoto Univ, Grad Sch Med Sci, Dept Diagnost Radiol, Kumamoto, Kumamoto, Japan

[2] Kumamoto Univ Hosp, Radiol, 1-1-1 Honjo,Chuo Ku, Kumamoto, Kumamoto 8608556, Japan

来源：

MAGNETIC RESONANCE IN MEDICAL SCIENCES | 2023年 / 22卷 / 02期

关键词：

artificial intelligence; deep learning; machine learning; magnetic resonance imaging; CONVOLUTIONAL NEURAL-NETWORK; RADIOMICS; APPROXIMATE; TUMOR; MODEL;

D O I：

10.2463/mrms.rev.2022-0102

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

The application of machine learning (ML) and deep learning (DL) in radiology has expanded exponen-tially. 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.

引用

页码：147 / 156

页数：10

共 72 条

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