Systematic review of artificial intelligence-based image diagnosis for inflammatory bowel disease

被引：12

作者：

Kawamoto, Ami ^{[1
]}

Takenaka, Kento ^{[1
]}

Okamoto, Ryuichi ^{[1
]}

Watanabe, Mamoru ^{[2
]}

Ohtsuka, Kazuo ^{[1
,3
]}

机构：

[1] Tokyo Med & Dent Univ, Dept Gastroenterol & Hepatol, Tokyo, Japan

[2] Tokyo Med & Dent Univ, TMDU Adv Res Inst, Tokyo, Japan

[3] Tokyo Med & Dent Univ, Endoscop Unit, Tokyo, Japan

来源：

DIGESTIVE ENDOSCOPY | 2022年 / 34卷 / 07期

关键词：

artificial intelligence; deep learning; inflammatory bowel disease; CROHNS-DISEASE; NEURAL-NETWORK; ENTEROGRAPHY; VALIDATION;

D O I：

10.1111/den.14334

中图分类号：

R57 [消化系及腹部疾病];

学科分类号：

摘要：

Objectives Diagnosis of inflammatory bowel diseases (IBD) involves combining clinical, laboratory, endoscopic, histologic, and radiographic data. Artificial intelligence (AI) is rapidly being developed in various fields of medicine, including IBD. Because a key part in the diagnosis of IBD involves evaluating imaging data, AI is expected to play an important role in this aspect in the coming decades. We conducted a systematic literature review to highlight the current advancement of AI in diagnosing IBD from imaging data. Methods We performed an electronic PubMed search of the MEDLINE database for studies up to January 2022 involving IBD and AI. Studies using imaging data as input were included, and nonimaging data were excluded. Results A total of 27 studies are reviewed, including 18 studies involving endoscopic images and nine studies involving other imaging data. Conclusion We highlight in this review the recent advancement of AI in diagnosing IBD from imaging data by summarizing the relevant studies, and discuss the future role of AI in clinical practice.

引用

页码：1311 / 1319

页数：9

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