A primer on artificial intelligence in pancreatic imaging

被引：21
作者：
Ahmed, Taha M. [1 ]
Kawamoto, Satomi [1 ]
Hruban, Ralph H. [2 ]
Fishman, Elliot K. [1 ]
Soyer, Philippe [3 ]
Chu, Linda C. [1 ]
机构：
[1] Johns Hopkins Univ, Sch Med, Johns Hopkins Hosp, Russell H Morgan Dept Radiol & Radiol Sci, Baltimore, MD 21287 USA
[2] Johns Hopkins Univ, Johns Hopkins Hosp, Sol Goldman Pancreat Res Ctr, Dept Pathol,Sch Med, Baltimore, MD 21287 USA
[3] Univ Paris Cite, Hop Cochin, AP HP, Fac Med,Dept Radiol, F-75006 Paris, France
来源：
DIAGNOSTIC AND INTERVENTIONAL IMAGING | 2023年 / 104卷 / 09期
关键词：
Artificial intelligence; Deep learning; Radiomics; Pancreas; FINE-NEEDLE-ASPIRATION; MUCINOUS NEOPLASM IPMN; CT RADIOMICS FEATURES; DUCTAL ADENOCARCINOMA; AUTOIMMUNE PANCREATITIS; NEUROENDOCRINE TUMORS; SURVIVAL PREDICTION; DIAGNOSIS; CANCER; DIFFERENTIATION;
D O I：
10.1016/j.diii.2023.03.002
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) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score. & COPY; 2023 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.
引用
收藏
页码：435 / 447
页数：13
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