胃と腸57巻9号

2022年08月発行

今月の主題 胃癌スクリーニングの課題と将来展望

主題

胃内視鏡検診におけるAIの活用

著者： 平澤俊明¹ 渡邊昌人¹ 東佑香¹ 中尾栄祐² 池之山洋平¹ 石岡充彬¹³ 並河健¹ 渡海義隆¹ 吉水祥一¹ 堀内裕介¹ 石山晃世志¹ 由雄敏之¹ 中島寛隆⁴ 多田智裕⁵⁶ 藤崎順子¹

所属機関： ¹がん研究会有明病院消化器内科 ²多根総合病院消化器内科 ³品川胃腸肛門内視鏡クリニック ⁴早期胃癌検診協会 ⁵ただともひろ胃腸科肛門科 ⁶株式会社AIメディカルサービス

ページ範囲：P.1165 - P.1174

文献概要

要旨●胃内視鏡検診の普及に伴い，検査の質を高い精度で均てん化させ，どのように精度管理を行っていくかが大きな課題となっている．一方，人工知能（AI）を用いた画像認識は，機械学習とdeep learningという革新的技術により飛躍的に発展した．胃内視鏡診断でもAIによる内視鏡観察部位診断，胃癌の存在診断・質的診断の研究が進められおり，医師と同等レベルの精度が報告されている．現在，AIによる二次読影支援および検査時のリアルタイム診断が臨床導入されようとしている．胃内視鏡検診の精度管理には，人間の力だけではなくAIの支援が有用であり，AIの導入により均てん化された質の高い内視鏡検査が期待される．

参考文献

1）国立がん研究センターがん予防・検診研究センター．有効性評価に基づく胃がん検診ガイドライン2014年度版．2015

2）細川治，新保卓郎，松田一夫，他．任意型内視鏡検診での胃がん死亡率減少効果．日消がん検診誌　49：401-407, 2011

3）Menon S, Trudgill N. How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis. Endosc Int Open　2：E46-50, 2014

4）Hosokawa O, Hattori M, Douden K, et al. Difference in accuracy between gastroscopy and colonoscopy for detection of cancer. Hepatogastroenterology　54：442-444, 2007

5）Yalamarthi S, Witherspoon P, McCole D, et al. Missed diagnoses in patients with upper gastrointestinal cancers. Endoscopy　36：874-879, 2004

6）Zhang Q, Chen ZY, Chen CD, et al. Training in early gastric cancer diagnosis improves the detection rate of early gastric cancer：an observational study in China. Medicine（Baltimore）　94：e384, 2015

7）Yamazato T, Oyama T, Yoshida T, et al. Two years' intensive training in endoscopic diagnosis facilitates detection of early gastric cancer. Intern Med　51：1461-1465, 2012

8）Yoshimizu S, Hirasawa T, Horiuchi Y, et al. Differences in upper gastrointestinal neoplasm detection rates based on inspection time and esophagogastroduodenoscopy training. Endosc Int Open　6：E1190-1197, 2018

9）Yao K, Uedo N, Kamada T, et al. Guidelines for endoscopic diagnosis of early gastric cancer. Dig Endosc　32：663-698, 2020

10）Takiyama H, Ozawa T, Ishihara S, et al. Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Sci Rep　8：7497, 2018

11）Wu L, Zhou W, Wan X, et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy　51：522-531, 2019

12）Wu L, Zhang J, Zhou W, et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut　68：2161-2169, 2019

13）Wu L, He X, Liu M, et al. Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer：a randomized controlled trial. Endoscopy　53：1199-1207, 2021

14）Chen D, Wu L, Li Y, et al. Comparing blind spots of unsedated ultrafine, sedated, and unsedated conventional gastroscopy with and without artificial intelligence：a prospective, single-blind, 3-parallel-group, randomized, single-center trial. Gastrointest Endosc　91：332-339, 2020

15）Kato S, Matsukura N, Tsukada K, et al. Helicobacter pylori infection-negative gastric cancer in Japanese hospital patients：incidence and pathological characteristics. Cancer Sci　98：790-794, 2007

16）Yoon H, Kim N, Lee HS, et al. Helicobacter pylori-negative gastric cancer in South Korea：incidence and clinicopathologic characteristics. Helicobacter　16：382-388, 2011

17）春間賢（監）．胃炎の京都分類，改訂第2版．日本メディカルセンター，2018

18）Watanabe K, Nagata N, Shimbo T, et al. Accuracy of endoscopic diagnosis of Helicobacter pylori infection according to level of endoscopic experience and the effect of training. BMC Gastroenterol　13：128, 2013

19）Shichijo S, Nomura S, Aoyama K, et al. Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBioMedicine　25：106-111, 2017

20）Shichijo S, Endo Y, Aoyama K, et al. Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images. Scand J Gastroenterol　54：158-163, 2019

21）Nakashima H, Kawahira H, Kawachi H, et al. Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning：a single-center prospective study（with video）. Gastric Cancer　23：1033-1040, 2020

22）Ikenoyama Y, Hirasawa T, Ishioka M, et al. Detecting early gastric cancer：Comparison between the diagnostic ability of convolutional neural networks and endoscopists. Dig Endosc　33：141-150, 2021

23）Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer　21：653-660, 2018

24）Ishioka M, Hirasawa T, Tada T. Detecting gastric cancer from video images using convolutional neural networks. Dig Endosc　31：e34-35, 2019

25）Yoon HJ, Kim S, Kim JH, et al. A lesion-based convolutional neural network improves endoscopic detection and depth prediction of early gastric cancer. J Clin Med　8：1310, 2019

26）Tang D, Wang L, Ling T, et al. Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer：A multicentre retrospective diagnostic study. EBioMedicine　62：103146, 2020

27）Namikawa K, Hirasawa T, Nakano K, et al. Artificial intelligence-based diagnostic system classifying gastric cancers and ulcers：comparison between the original and newly developed systems. Endoscopy　52：1077-1083, 2020

28）Ezoe Y, Muto M, Uedo N, et al. Magnifying narrowband imaging is more accurate than conventional white-light imaging in diagnosis of gastric mucosal cancer. Gastroenterology　141：2017-2025, 2011

29）Yao K, Doyama H, Gotoda T, et al. Diagnostic performance and limitations of magnifying narrow-band imaging in screening endoscopy of early gastric cancer：a prospective multicenter feasibility study. Gastric Cancer　17：669-679, 2014

30）Horiuchi Y, Aoyama K, Tokai Y, et al. Convolutional neural network for differentiating gastric cancer from gastritis using magnified endoscopy with narrow band imaging. Dig Dis Sci　65：1355-1363, 2020

31）Ueyama H, Kato Y, Akazawa Y, et al. Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging. J Gastroenterol Hepatol　36：482-489, 2021

32）Horiuchi Y, Hirasawa T, Ishizuka N, et al. Performance of a computer-aided diagnosis system in diagnosing early gastric cancer using magnifying endoscopy videos with narrow-band imaging（with videos）. Gastrointest Endosc　92：856-865, 2020

33）Ling T, Wu L, Fu Y, et al. A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy. Endoscopy　53：469-477, 2021

34）Hu H, Gong L, Dong D, et al. Identifying early gastric cancer under magnifying narrow-band images with deep learning：a multicenter study. Gastrointest Endosc　93：1333-1341, 2021

35）Wu L, Wang J, He X, et al. Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status（with videos）. Gastrointest Endosc　95：92-104, 2022

36）日本消化器がん検診学会対策型検診のための胃内視鏡検診マニュアル作成委員会（編）．対策型検診のための胃内視鏡検診マニュアル．南江堂，2017

37）Oura H, Matsumura T, Fujie M, et al. Development and evaluation of a double-check support system using artificial intelligence in endoscopic screening for gastric cancer. Gastric Cancer　25：392-400, 2022

38）厚生労働省．プログラムの医療機器への該当性に関する基本的な考え方について（薬食監麻発1114第5号 平成26年11月14日付）．2014

39）厚生労働省．人工知能（AI）を用いた診断，治療等の支援を行うプログラムの利用と医師法第17条の規定との関係について（医政医発1219第1号 平成30年12月19日付）．2018