Artificial Intelligence in Neuroimaging: Clinical Applications

被引：11

作者：

Choi, Kyu Sung ^{[1
,2
]}

Sunwoo, Leonard ^{[3
,4
]}

机构：

[1] Seoul Natl Univ Hosp, Dept Radiol, Seoul, South Korea

[2] Seoul Natl Univ Hosp, Artificial Intelligence Collaborat Network, Seoul, South Korea

[3] Seoul Natl Univ, Dept Radiol, Bundang Hosp, 82 Guam Ro, Seongnam 13620, Gyeonggi Do, South Korea

[4] Seoul Natl Univ, Ctr Artificial Intelligence Healthcare, Bundang Hosp, 82 Guam Ro, Seongnam 13620, Gyeonggi Do, South Korea

来源：

INVESTIGATIVE MAGNETIC RESONANCE IMAGING | 2022年 / 26卷 / 01期

关键词：

Artificial intelligence; Deep learning; Radiomics; Neuroimaging; Clinical application; DEEP-LEARNING ALGORITHM; BRAIN METASTASES; CLASSIFICATION; PERFORMANCE; DIAGNOSIS; THERAPY; NETWORK; DISEASE;

D O I：

10.13104/imri.2022.26.1.1

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：

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.

引用

页码：1 / 9

页数：9

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