Current Applications and Future Impact of Machine Learning in Radiology

被引：560

作者：

Choy, Garry ^{[1
]}

Khalilzadeh, Omid ^{[1
,3
]}

Michalski, Mark ^{[1
]}

Do, Synho ^{[1
]}

Samir, Anthony E. ^{[1
]}

Pianykh, Oleg S. ^{[1
]}

Geis, J. Raymond ^{[2
]}

Pandharipande, Pari V. ^{[1
]}

Brink, James A. ^{[1
]}

Dreyer, Keith J. ^{[1
]}

机构：

[1] Harvard Med Sch, Massachusetts Gen Hosp, Dept Radiol, 55 Fruit St, Boston, MA 02114 USA

[2] Univ Colorado, Sch Med, Dept Radiol, Aurora, CO USA

[3] Icahn Sch Med Mt Sinai, Mt Sinai Hlth Syst, Dept Radiol, New York, NY 10029 USA

来源：

RADIOLOGY | 2018年 / 288卷 / 02期

关键词：

CONVOLUTIONAL NEURAL-NETWORK; FREE DIFFUSION MRI; ARTIFICIAL-INTELLIGENCE; SEGMENTATION METHODS; AIDED DIAGNOSIS; PROSTATE-CANCER; CT IMAGES; CLASSIFICATION; PATIENT; LESIONS;

D O I：

10.1148/radiol.2018171820

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：

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed. (c) RSNA, 2018

引用

页码：318 / 328

页数：11

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