icon fsr

文献詳細

雑誌文献

胃と腸57巻10号

2022年09月発行

今月の主題 大腸腫瘍診療の最前線

主題

人工知能(AI)を用いた大腸内視鏡検査の最前線—質的診断

著者: 森悠一12 工藤進英1 三澤将史1

所属機関: 1昭和大学横浜市北部病院消化器センター 2オスロ大学Institute of Health and Society

ページ範囲:P.1305 - P.1311

文献概要

要旨●近年の飛躍的なテクノロジーの進化に伴い,内視鏡医が取得する画像の質・データ量は格段に向上している.しかし,求められる診断技術のハードルも同時に向上しており,高精度の内視鏡診断が一部の熟練医に限られているのも事実である.このような先端機器と医療技術のギャップを解決することを目的として,人工知能(AI)を用いたコンピュータ診断支援システムの研究開発が注目を浴びており,本邦でも一部製品の市販が始まっている.本稿では,大腸内視鏡AIの研究状況を概観するとともに,薬事承認の状況についても紹介し,今後の診療現場に内視鏡AIがどのような影響を与えうるのかを考察する.

参考文献

1)Ladabaum U, Fioritto A, Mitani A, et al. Real-time optical biopsy of colon polyps with narrow band imaging in community practice does not yet meet key thresholds for clinical decisions. Gastroenterology 144:81-91, 2013
2)Tischendorf JJW, Gross S, Winograd R, et al. Computer-aided classification of colorectal polyps based on vascular patterns:a pilot study. Endoscopy 42:203-207, 2010
3)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
4)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
5)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
6)Chen PJ, Lin MC, Lai MJ, et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology 154:568-575, 2018
7)Minegishi Y, Kudo SE, Miyata Y, et al. Comprehensive diagnostic performance of real-time characterization of colorectal lesions using an artificial intelligence-assisted system:A prospective study. Gastroenterology 163:323-325, 2022
8)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
9)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
10)Mori Y, Kudo SE, Chiu PWY, et al. Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions:an international web-based study. Endoscopy 48:1110-1118, 2016
11)Mori Y, Kudo SE, Wakamura K, et al. Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy(with videos). Gastrointest Endosc 81:621-629, 2015
12)Misawa M, Kudo SE, Mori Y, et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology 150:1531-1532, 2016
13)Misawa M, Kudo SE, Mori Y, et al. Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions:comparison with experts. Int J Comput Assist Radiol Surg 12:757-766, 2017
14)Mori Y, Kudo SE, Misawa M, et al. Simultaneous detection and characterization of diminutive polyps with the use of artificial intelligence during colonoscopy. VideoGIE 4:7-10, 2019
15)Mori Y, Kudo SE, Mori K. Potential of artificial intelligence-assisted colonoscopy using an endocytoscope(with video). Dig Endosc 30(Suppl 1):52-53, 2018
16)Mori Y, Kudo SE, 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
17)Barua I, Wieszczy P, Kudo S-e, et al. Real-time artificial intelligence-based optical diagnosis of neoplastic polyps during colonoscopy. NEJM Evidence 1:EVIDoa2200003, 2022
18)Mori Y, Kudo SE, Misawa M, et al. Artificial intelligence-assisted colonic endocytoscopy for cancer recognition:a multicenter study. Endosc Int Open 9:E1004-1011, 2021
19)Kuiper T, Alderlieste YA, Tytgat KMAJ, et al. Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence. Endoscopy 47:56-62, 2015
20)Rath T, Tontini GE, Vieth M, et al. In vivo real-time assessment of colorectal polyp histology using an optical biopsy forceps system based on laser-induced fluorescence spectroscopy. Endoscopy 48:557-562, 2016
21)Aihara H, Saito S, Inomata H, et al. Computer-aided diagnosis of neoplastic colorectal lesions using ‘real-time' numerical color analysis during autofluorescence endoscopy. Eur J Gastroenterol Hepatol 25:488-494, 2013
22)Horiuchi H, Tamai N, Kamba S, et al. Real-time computer-aided diagnosis of diminutive rectosigmoid polyps using an auto-fluorescence imaging system and novel color intensity analysis software. Scand J Gastroenterol 54:800-805, 2019
23)Mori Y, Kudo SE, Berzin TM, et al. Computer-aided diagnosis for colonoscopy. Endoscopy 49:813-819, 2017
24)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
25)Renner J, Phlipsen H, Haller B, et al. Optical classification of neoplastic colorectal polyps—a computer-assisted approach(the COACH study). Scand J Gastroenterol 53:1100-1106, 2018
26)Chinzei K, Shimizu A, Mori K, et al. Regulatory science on AI-based medical devices and systems. Advanced Biomedical Engineering 7:118-123, 2018

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

本サービスは医療関係者に向けた情報提供を目的としております。
一般の方に対する情報提供を目的としたものではない事をご了承ください。
また,本サービスのご利用にあたっては,利用規約およびプライバシーポリシーへの同意が必要です。

※本サービスを使わずにご契約中の電子商品をご利用したい場合はこちら