Artificial Intelligence in Nuclear Medicine

被引：118

作者：

Nensa, Felix ^{[1
]}

Demircioglu, Aydin ^{[1
]}

Rischpler, Christoph ^{[2
]}

机构：

[1] Univ Duisburg Essen, Univ Hosp Essen, Dept Diagnost & Intervent Radiol & Neuroradiol, Hufelandstr 55, D-45147 Essen, Germany

[2] Univ Duisburg Essen, Univ Hosp Essen, Dept Nucl Med, Essen, Germany

来源：

JOURNAL OF NUCLEAR MEDICINE | 2019年 / 60卷

关键词：

artificial intelligence; machine learning; deep learning; nuclear medicine; medical imaging; CONVOLUTIONAL NEURAL-NETWORK; MODEL; CLASSIFICATION; RECONSTRUCTION; RADIOLOGY; DISEASE; CANCER; IMAGES; PET/CT; CT;

D O I：

10.2967/jnumed.118.220590

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：

Despite the great media attention for artificial intelligence (AI), for many health care professionals the term and the functioning of AI remain a "black box,"leading to exaggerated expectations on the one hand and unfounded fears on the other. In this review, we provide a conceptual classification and a brief summary of the technical fundamentals of AI. Possible applications are discussed on the basis of a typical work flow in medical imaging, grouped by planning, scanning, interpretation, and reporting. The main limitations of current AI techniques, such as issues with interpretability or the need for large amounts of annotated data, are briefly addressed. Finally, we highlight the possible impact of AI on the nuclear medicine profession, the associated challenges and, last but not least, the opportunities.

引用

页码：29S / 37S

页数：9

共 78 条

[1]

[Anonymous], 2018, Journal of Digital Imaging

[2]

Ansart M, 2019, STAT METHODS MED RES

[3] Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network [J].