icon fsr

文献詳細

雑誌文献

胃と腸56巻4号

2021年04月発行

文献概要

今月の主題 消化管疾患AI診断の現状 主題

大腸疾患におけるAI診断

著者: 三澤将史1 工藤進英1 森悠一1 小川悠史1 前田康晴1 武田健一1 一政克朗1 中村大樹1 工藤豊樹1 久行友和1 若村邦彦1 林武雅1 宮地英行1 馬場俊之1 石田文生1 根本哲生2

所属機関: 1昭和大学横浜市北部病院消化器センター 2昭和大学横浜市北部病院臨床病理診断科

ページ範囲:P.451 - P.461

文献購入ページに移動
要旨●人工知能(AI)技術の急速な進歩により,日常生活だけではなく内視鏡診療においてもわれわれがAIに触れる場面が増えてきている.大腸内視鏡AIにおいては,その研究開発のスピードには目を見張るものがあり,既にいくつかの製品が規制をクリアし診療で使用できるようになっている.現在研究が進んでいるのは2つのカテゴリーである.すなわちAIによる病変の拾い上げ診断支援(computer-aided detection)と病変の質的診断支援(computer-aided diagnosis)である.いずれも専門医に匹敵する精度が論文などで報告されているが,AIの仕組みそのものに起因する限界があり,過度な期待は禁物である.本稿ではこれまでに報告されている大腸内視鏡AIをレビューし,その有用性と限界について論じる.

参考文献

1)Zauber AG, Winawer SJ, O'Brien MJ, et al. Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N Engl J Med 366:687-696, 2012
2)van Rijn JC, Reitsma JB, Stoker J, et al. Polyp miss rate determined by tandem colonoscopy:a systematic review. Am J Gastroenterol 101:343-350, 2006
3)le Clercq CM, Bouwens MW, Rondagh EJ, et al. Postcolonoscopy colorectal cancers are preventable:a population-based study. Gut 63:957-963, 2014
4)Misawa M, Kudo S, Mori Y, et al. Artificial intelligence-assisted polyp detection for colonoscopy:initial experience. Gastroenterology 154:2027-2029, 2018
5)Urban G, Tripathi P, Alkayali T, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 155:1069-1078, 2018
6)Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates:a prospective randomised controlled study. Gut 68:1813-1819, 2019
7)Barua I, Vinsard DG, Jodal HC, et al. Artificial intelligence for polyp detection during colonoscopy:a systematic review and meta-analysis. Endoscopy 2020[Epub ahead of print]
8)Misawa M, Kudo S, Mori Y, et al. Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database(with video). Gastrointest Endosc 2020[Epub ahead of print]
9)Ma R, Wang R, Pizer S, et al. Real-time 3D reconstruction of colonoscopic surfaces for determining missing regions. In Shen, D, Liu, T, Peters, TM, et al(eds). Medical Image Computing and Computer Assisted Intervention—MICCAI 2019. Springer, Berlin, pp 573-582, 2019
10)味岡洋一,林俊壱,渡辺英伸,他.大腸腫瘍のミクロとマクロの対比における新しい知見—pit patternのフラクタル解析と組織所見との対比.胃と腸 34:1599-1606, 1999
11)Takemura Y, Yoshida S, Tanaka S, et al. Quantitative analysis and development of a computer-aided system for identification of regular pit patterns of colorectal lesions. Gastrointest Endosc 72:1047-1051, 2010
12)Kominami Y, Yoshida S, Tanaka S, et al. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc 83:643-649, 2016
13)Chen PJ, Lin MC, Lai MJ, et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology 154:568-575, 2018
14)Byrne MF, Chapados N, Soudan F, et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 68:94-100, 2019
15)Komeda Y, Handa H, Watanabe T, et al. Computer-aided diagnosis based on convolutional neural network system for colorectal polyp classification:preliminary experience. Oncology 93(Suppl 1):30-34, 2017
16)Tamai N, Saito Y, Sakamoto T, et al. Effectiveness of computer-aided diagnosis of colorectal lesions using novel software for magnifying narrow-band imaging:a pilot study. Endosc Int Open 5:E690-694, 2017
17)Shimura T, Ebi M, Yamada T, et al. Magnifying chromoendoscopy and endoscopic ultrasonography measure invasion depth of early stage colorectal cancer with equal accuracy on the basis of a prospective trial. Clin Gastroenterol Hepatol 12:662-668, 2014
18)Ozawa T, Ishihara S, Fujishiro M, et al. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks. Therap Adv Gastroenterol 13:1756284820910659, 2020
19)工藤進英,若村邦彦,池原伸直,他.大腸癌の質的・量的診断—超拡大内視鏡を用いた大腸腫瘍診断.Intestine 13:173-180, 2009
20)Kudo S, Wakamura K, Ikehara N, et al. Diagnosis of colorectal lesions with a novel endocytoscopic classification-a pilot study. Endoscopy 43:869-875, 2011
21)Kudo S, Misawa M, Wada Y, et al. Endocytoscopic microvasculature evaluation is a reliable new diagnostic method for colorectal lesions(with video). Gastrointest Endosc 82:912-923, 2015
22)Kudo T, Kudo S, Wakamura K, et al. Diagnostic performance of endocytoscopy for evaluating the invasion depth of different morphological types of colorectal tumors. Dig Endosc 27:755-762, 2015
23)Mori Y, Kudo S, Wakamura K, et al. Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy(with videos). Gastrointest Endosc 81:621-629, 2015
24)Mori Y, Kudo S, Chiu PW, et al. Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions:an international web-based study. Endoscopy 48:1110-1118, 2016
25)Misawa M, Kudo S, Mori Y, et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology 150:1531-1532, 2016
26)Kudo S, Misawa M, Mori Y, et al. Artificial Intelligence-assisted System Improves Endoscopic Identification of Colorectal Neoplasms. Clin Gastroenterol Hepatol 18:1874-1881, 2020
27)Mori Y, Kudo S, Misawa M, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy:a prospective study. Ann Intern Med 169:357-366, 2018
28)Takeda K, Kudo S, Mori Y, et al. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy. Endoscopy 49:798-802, 2017
29)Maeda Y, Kudo S, Mori Y, et al. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis(with video). Gastrointest Endosc 89:408-415, 2019
30)Liu Y, Chen PC, Krause J, et al. How to read articles that use machine learning:users' guides to the medical literature. JAMA 322:1806-1816, 2019

掲載誌情報

出版社:株式会社医学書院

電子版ISSN:1882-1219

印刷版ISSN:0536-2180

雑誌購入ページに移動
icon up
あなたは医療従事者ですか?