
随着人们对消化内镜知识了解的增多,行胃肠镜检查的患者逐年增加,但消化内镜的诊疗需要大量的经验积累,准确的诊断对于低年资医师而言是个挑战。目前基于深度学习技术的人工智能模型在消化内镜领域应用广泛,可以提高观察者间的一致性,能够提高临床疾病的检出率及诊断正确率,合理的应用可以提高临床工作效率,减轻患者经济负担。本文就人工智能在消化内镜中的应用进行综述,旨为消化内镜诊疗工作提供思路。
","endNoteUrl_en":"http://xuebao.sdfmu.edu.cn/EN/article/getTxtFile.do?fileType=EndNote&id=715","reference":"1 | Yoshii S, Mabe K, Watano K, et al. Validity of endoscopic features for the diagnosis of Helicobacter pylori infection status based on the Kyoto classification of gastritis[J]. Dig Endosc, 2020, 32(1): 74. |
2 | 中华医学会肿瘤学分会早诊早治学组. 中国食管癌早诊早治专家共识[J]. 中华肿瘤杂志, 2022, 44(10): 1066. |
3 | 乔隽, 褚传莲. 早期食管癌内镜诊断技术的进展[J]. 临床消化病杂志, 2021, 33(4): 299. |
4 | Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[J]. Gastrointest Endosc, 2019, 89(1): 25. |
5 | Yang XX, Li Z, Shao XJ, et al. Real-time artificial intelligence for endoscopic diagnosis of early esophageal squamous cell cancer (with video)[J]. Dig Endosc, 2021, 33(7): 1075. |
6 | Oyama T, Momma K, Makuuchi H. Japan esophageal society classification of superficial esophageal squamous cell carcinoma[J]. Endosc Dig, 2012, 24: 466. |
7 | Oyama T, Inoue H, Arima M, et al. Prediction of the invasion depth of superficial squamous cell carcinoma based on microvessel morphology: magnifying endoscopic classification of the Japan esophageal society[J]. Esophagus, 2017, 14(2): 105. |
8 | Yuan XL, Liu W, Liu Y, et al. Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study[J]. Surg Endosc, 2022, 36(11): 8651. |
9 | 国家消化系统疾病临床医学研究中心, 中华医学会消化内镜学分会, 中国医师协会消化医师分会. 中国巴雷特食管及其早期腺癌筛查与诊治共识(2017,万宁)[J]. 中华内科杂志, 2017, 56(9): 701. |
10 | Wilson KT, Fu S, Ramanujam KS, et al. Increased expression of inducible nitric oxide synthase and cyclooxygenase-2 in Barrett's esophagus and associated adenocarcinomas[J]. Cancer Res, 1998, 58(14): 2929. |
11 | Tsai MC, Yen HH, Tsai HY, et al. Artificial intelligence system for the detection of Barrett's esophagus[J]. World J Gastroenterol, 2023, 29(48): 6198. |
12 | Hashimoto R, Requa J, Dao T, et al. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video)[J]. Gastrointest Endosc, 2020, 91(6): 1264. |
13 | Iwagami H, Ishihara R, Aoyama K, et al. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma[J]. J Gastroenterol Hepatol, 2021, 36(1): 131. |
14 | Kanemitsu T, Yao K, Nagahama T, et al. Extending magnifying NBI diagnosis of intestinal metaplasia in the stomach: the white opaque substance marker[J]. Endoscopy, 2017, 49(6): 529. |
15 | Kimura K, Takemoto T. An endoscopic recognition of the atrophic borderand its significance in chronic gastritis[J]. Endoscopy, 1969, 1(3): 87. |
16 | Luo J, Cao S, Ding N, et al. A deep learning method to assist with chronic atrophic gastritis diagnosis using white light images[J]. Dig Liver Dis, 2022, 54(11): 1513. |
17 | Zhang JH, Yu JH, Fu SN, et al. Adoption value of deep learning and serological indicators in the screening of atrophic gastritis based on artificial intelligence[J]. J Supercomput, 2021, 77(8): 8674. |
18 | Wu LL, Zhang J, Zhou W, et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy[J]. Gut, 2019, 68(12): 2161. |
19 | Thrift AP, Wenker TN, El-Serag HB. Global burden of gastric cancer: epidemiological trends, risk factors, screening and prevention[J]. Nat Rev Clin Oncol, 2023, 20(5): 338. |
20 | Itoh T, Kawahira H, Nakashima H, et al. Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images[J]. Endosc Int Open, 2018, 6(2): E139. |
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)[J]. Gastric Cancer, 2020, 23(6): 1033. |
22 | 中华医学会肿瘤学分会早诊早治学组. 胃癌早诊早治中国专家共识(2023版)[J]. 中华消化外科杂志, 2024, 23(1): 23. |
23 | Hu YY, Lian QW, Lin ZH, et al. Diagnostic performance of magnifying narrow-band imaging for early gastric cancer: a meta-analysis[J]. World J Gastroenterol, 2015, 21(25): 7884. |
24 | 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]. J Gastroenterol Hepatol, 2021, 36(2): 482. |
25 | He XQ, Wu LL, Dong ZH, et al. Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy:a multicenter diagnostic study(with videos)[J]. Gastrointest Endosc, 2022, 95(4): 671. |
26 | Inoue S, Shichijo S, Aoyama K, et al. Application of convolutional neural networks for detection of superficial nonampullary duodenal epithelial tumors in esophagogastroduodenoscopic images[J]. Clin Transl Gastroenterol, 2020, 11(3): e00154. |
27 | Stassen PMC, de Jonge PJF, Webster GJM, et al. Clinical practice patterns in indirect peroral cholangiopancreatoscopy: outcome of a European survey[J]. Endosc Int Open, 2021, 9(11): E1704. |
28 | Saraiva MM, Ribeiro T, González-Haba M, et al. Deep learning for automatic diagnosis and morphologic characterization of malignant biliary strictures using digital cholangioscopy: a multicentric study[J]. Cancers (Basel), 2023, 15(19): 4827. |
29 | Kim T, Kim J, Choi HS, et al. Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation[J]. Sci Rep, 2021, 11(1): 8381. |
30 | Minoda Y, Ihara E, Fujimori N, et al. Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors[J]. Sci Rep, 2022, 12(1): 16640. |
31 | Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023[J]. CA Cancer J Clin, 2023, 73(1): 17. |
32 | Tonozuka R, Itoi T, Nagata N, et al. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study[J]. J Hepatobiliary Pancreat Sci, 2021, 28(1): 95. |
33 | European Study Group on Cystic Tumours of the Pancreas. European evidence-based guidelines on pancreatic cystic neoplasms[J]. Gut, 2018, 67(5): 789. |
34 | Kuwahara T, Hara K, Mizuno N, et al. Usefulness of deep learning analysis for the diagnosis of malignancy in intraductal papillary mucinous neoplasms of the pancreas[J]. Clin Transl Gastroenterol, 2019, 10(5): 1. |
35 | Jia X, Meng MQH. A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images[C]//2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Manhattan: IEEE, 2016: 639. |
36 | Leenhardt R, Vasseur P, Li C, et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy[J]. Gastrointest Endosc, 2019, 89(1): 189. |
37 | Saito H, Aoki T, Aoyama K, et al. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network[J]. Gastrointest Endosc, 2020, 92(1): 144. |
38 | Martins M, Mascarenhas M, Afonso J, et al. Deep-learning and device-assisted enteroscopy: automatic panendoscopic detection of ulcers and erosions[J]. Medicina (Kaunas), 2023, 59(1): 172. |
39 | Mascarenhas Saraiva M, Ribeiro T, Afonso J, et al. Deep learning and device-assisted enteroscopy: automatic detection of gastrointestinal angioectasia[J]. Medicina (Kaunas), 2021, 57(12): 1378. |
40 | Schottinger JE, Jensen CD, Ghai NR, et al. Association of physician adenoma detection rates with postcolonoscopy colorectal cancer[J]. JAMA, 2022, 327(21): 2114. |
41 | Xu L, He XJ, Zhou JB, et al. Artificial intelligence-assisted colonoscopy: a prospective, multicenter, randomized controlled trial of polyp detection[J]. Cancer Med, 2021, 10(20): 7184. |
42 | Komeda Y, Handa H, Matsui R, et al. Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks[J]. PLoS One, 2021, 16(6): e0253585. |
43 | Jin EH, Lee D, Bae JH, et al. Improved accuracy in optical diagnosis of colorectal polyps using convolutional neural networks with visual explanations[J]. Gastroenterology, 2020, 158(8): 2169. |
44 | Takeda K, Kudo SE, Mori Y, et al. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy[J]. Endoscopy, 2017, 49(8): 798. |
45 | Tokunaga M, Matsumura T, Nankinzan R, et al. Computer-aided diagnosis system using only white-light endoscopy for the prediction of invasion depth in colorectal cancer[J]. Gastrointest Endosc, 2021, 93(3): 647. |
46 | Bai JW, Liu K, Gao L, et al. Computer-aided diagnosis in predicting the invasion depth of early colorectal cancer: a systematic review and meta-analysis of diagnostic test accuracy[J]. Surg Endosc, 2023, 37(9): 6627. |
47 | Ozawa T, Ishihara S, Fujishiro M, et al. Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis[J]. Gastrointest Endosc, 2019, 89(2): 416. |
48 | Stidham RW, Liu WS, Bishu S, et al. Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis[J]. JAMA Netw Open, 2019, 2(5): e193963. |
49 | Mossotto E, Ashton JJ, Coelho T, et al. Classification of paediatric inflammatory bowel disease using machine learning[J]. Sci Rep, 2017, 7(1): 2427. |
With the increasing knowledge of gastrointestinal endoscopy, the number of patients undergoing gastrointestinal endoscopy is increasing year by year, but the diagnosis and treatment of gastrointestinal endoscopy requires a lot of experience accumulation, and the correct diagnosis is a challenge for junior doctors. At present, the artificial intelligence model based on deep learning technology is widely used in the field of gastrointestinal endoscopy, which can improve the consistency between observers, improve the detection rate and diagnostic accuracy of clinical diseases, and reasonable application can improve the clinical work efficiency and reduce the economic burden of patients. This article reviews the application of artificial intelligence in gastrointestinal endoscopy, in order to provide ideas for the diagnosis and treatment of gastrointestinal endoscopy.
","bibtexUrl_en":"http://xuebao.sdfmu.edu.cn/EN/article/getTxtFile.do?fileType=BibTeX&id=715","abstractUrl_cn":"http://xuebao.sdfmu.edu.cn/CN/10.3969/j.issn.2097-0005.2024.11.012","zuoZheCn_L":"王浩, 刘虹, 李国栋","juanUrl_cn":"http://xuebao.sdfmu.edu.cn/CN/Y2024","lanMu_en":"Reviews","qiUrl_en":"//www.pitakata.com/xuebao/EN/Y2024/V45/I11","zuoZhe_EN":"Hao WANG, Hong LIU, Guodong LI(Application of artificial intelligence technology in gastrointestinal endoscopy
Hao WANG, Hong LIU, Guodong LI
Journal of ShanDong First Medical University&ShanDong Academy of Medical Sciences››2024, Vol. 45››Issue (11): 700-704.
Application of artificial intelligence technology in gastrointestinal endoscopy
With the increasing knowledge of gastrointestinal endoscopy, the number of patients undergoing gastrointestinal endoscopy is increasing year by year, but the diagnosis and treatment of gastrointestinal endoscopy requires a lot of experience accumulation, and the correct diagnosis is a challenge for junior doctors. At present, the artificial intelligence model based on deep learning technology is widely used in the field of gastrointestinal endoscopy, which can improve the consistency between observers, improve the detection rate and diagnostic accuracy of clinical diseases, and reasonable application can improve the clinical work efficiency and reduce the economic burden of patients. This article reviews the application of artificial intelligence in gastrointestinal endoscopy, in order to provide ideas for the diagnosis and treatment of gastrointestinal endoscopy.
gastrointestinal endoscopy/artificial intelligence/deep learning
1 | Yoshii S, Mabe K, Watano K, et al. Validity of endoscopic features for the diagnosis ofHelicobacter pyloriinfection status based on the Kyoto classification of gastritis[J].Dig Endosc,2020,32(1): 74. |
2 | 中华医学会肿瘤学分会早诊早治学组. 中国食管癌早诊早治专家共识[J].中华肿瘤杂志,2022,44(10): 1066. |
3 | 乔隽, 褚传莲. 早期食管癌内镜诊断技术的进展[J].临床消化病杂志,2021,33(4): 299. |
4 | Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[J].Gastrointest Endosc,2019,89(1): 25. |
5 | Yang XX, Li Z, Shao XJ, et al. Real-time artificial intelligence for endoscopic diagnosis of early esophageal squamous cell cancer (with video)[J].Dig Endosc,2021,33(7): 1075. |
6 | Oyama T, Momma K, Makuuchi H. Japan esophageal society classification of superficial esophageal squamous cell carcinoma[J].Endosc Dig,2012,24: 466. |
7 | Oyama T, Inoue H, Arima M, et al. Prediction of the invasion depth of superficial squamous cell carcinoma based on microvessel morphology: magnifying endoscopic classification of the Japan esophageal society[J].Esophagus,2017,14(2): 105. |
8 | Yuan XL, Liu W, Liu Y, et al. Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study[J].Surg Endosc,2022,36(11): 8651. |
9 | 国家消化系统疾病临床医学研究中心, 中华医学会消化内镜学分会, 中国医师协会消化医师分会. 中国巴雷特食管及其早期腺癌筛查与诊治共识(2017,万宁)[J].中华内科杂志,2017,56(9): 701. |
10 | Wilson KT, Fu S, Ramanujam KS, et al. Increased expression of inducible nitric oxide synthase and cyclooxygenase-2 in Barrett's esophagus and associated adenocarcinomas[J].Cancer Res,1998,58(14): 2929. |
11 | Tsai MC, Yen HH, Tsai HY, et al. Artificial intelligence system for the detection of Barrett's esophagus[J].World J Gastroenterol,2023,29(48): 6198. |
12 | Hashimoto R, Requa J, Dao T, et al. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video)[J].Gastrointest Endosc,2020,91(6): 1264. |
13 | Iwagami H, Ishihara R, Aoyama K, et al. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma[J].J Gastroenterol Hepatol,2021,36(1): 131. |
14 | Kanemitsu T, Yao K, Nagahama T, et al. Extending magnifying NBI diagnosis of intestinal metaplasia in the stomach: the white opaque substance marker[J].Endoscopy,2017,49(6): 529. |
15 | Kimura K, Takemoto T. An endoscopic recognition of the atrophic borderand its significance in chronic gastritis[J].Endoscopy,1969,1(3): 87. |
16 | Luo J, Cao S, Ding N, et al. A deep learning method to assist with chronic atrophic gastritis diagnosis using white light images[J].Dig Liver Dis,2022,54(11): 1513. |
17 | Zhang JH, Yu JH, Fu SN, et al. Adoption value of deep learning and serological indicators in the screening of atrophic gastritis based on artificial intelligence[J].J Supercomput,2021,77(8): 8674. |
18 | Wu LL, Zhang J, Zhou W, et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy[J].Gut,2019,68(12): 2161. |
19 | Thrift AP, Wenker TN, El-Serag HB. Global burden of gastric cancer: epidemiological trends, risk factors, screening and prevention[J].Nat Rev Clin Oncol,2023,20(5): 338. |
20 | Itoh T, Kawahira H, Nakashima H, et al. Deep learning analyzesHelicobacter pyloriinfection by upper gastrointestinal endoscopy images[J].Endosc Int Open,2018,6(2): E139. |
21 | Nakashima H, Kawahira H, Kawachi H, et al. Endoscopic three-categorical diagnosis ofHelicobacter pyloriinfection using linked color imaging and deep learning: a single-center prospective study (with video)[J].Gastric Cancer,2020,23(6): 1033. |
22 | 中华医学会肿瘤学分会早诊早治学组. 胃癌早诊早治中国专家共识(2023版)[J].中华消化外科杂志,2024,23(1): 23. |
23 | Hu YY, Lian QW, Lin ZH, et al. Diagnostic performance of magnifying narrow-band imaging for early gastric cancer: a meta-analysis[J].World J Gastroenterol,2015,21(25): 7884. |
24 | 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].J Gastroenterol Hepatol,2021,36(2): 482. |
25 | He XQ, Wu LL, Dong ZH, et al. Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy:a multicenter diagnostic study(with videos)[J].Gastrointest Endosc,2022,95(4): 671. |
26 | Inoue S, Shichijo S, Aoyama K, et al. Application of convolutional neural networks for detection of superficial nonampullary duodenal epithelial tumors in esophagogastroduodenoscopic images[J].Clin Transl Gastroenterol,2020,11(3): e00154. |
27 | Stassen PMC, de Jonge PJF, Webster GJM, et al. Clinical practice patterns in indirect peroral cholangiopancreatoscopy: outcome of a European survey[J].Endosc Int Open,2021,9(11): E1704. |
28 | Saraiva MM, Ribeiro T, González-Haba M, et al. Deep learning for automatic diagnosis and morphologic characterization of malignant biliary strictures using digital cholangioscopy: a multicentric study[J].Cancers (Basel),2023,15(19): 4827. |
29 | Kim T, Kim J, Choi HS, et al. Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation[J].Sci Rep,2021,11(1): 8381. |
30 | Minoda Y, Ihara E, Fujimori N, et al. Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors[J].Sci Rep,2022,12(1): 16640. |
31 | Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023[J].CA Cancer J Clin,2023,73(1): 17. |
32 | Tonozuka R, Itoi T, Nagata N, et al. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study[J].J Hepatobiliary Pancreat Sci,2021,28(1): 95. |
33 | European Study Group on Cystic Tumours of the Pancreas. European evidence-based guidelines on pancreatic cystic neoplasms[J].Gut,2018,67(5): 789. |
34 | Kuwahara T, Hara K, Mizuno N, et al. Usefulness of deep learning analysis for the diagnosis of malignancy in intraductal papillary mucinous neoplasms of the pancreas[J].Clin Transl Gastroenterol,2019,10(5): 1. |
35 | Jia X, Meng MQH. A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images[C]//2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Manhattan: IEEE,2016: 639. |
36 | Leenhardt R, Vasseur P, Li C, et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy[J].Gastrointest Endosc,2019,89(1): 189. |
37 | Saito H, Aoki T, Aoyama K, et al. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network[J].Gastrointest Endosc,2020,92(1): 144. |
38 | Martins M, Mascarenhas M, Afonso J, et al. Deep-learning and device-assisted enteroscopy: automatic panendoscopic detection of ulcers and erosions[J].Medicina (Kaunas),2023,59(1): 172. |
39 | Mascarenhas Saraiva M, Ribeiro T, Afonso J, et al. Deep learning and device-assisted enteroscopy: automatic detection of gastrointestinal angioectasia[J].Medicina (Kaunas),2021,57(12): 1378. |
40 | Schottinger JE, Jensen CD, Ghai NR, et al. Association of physician adenoma detection rates with postcolonoscopy colorectal cancer[J].JAMA,2022,327(21): 2114. |
41 | Xu L, He XJ, Zhou JB, et al. Artificial intelligence-assisted colonoscopy: a prospective, multicenter, randomized controlled trial of polyp detection[J].Cancer Med,2021,10(20): 7184. |
42 | Komeda Y, Handa H, Matsui R, et al. Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks[J].PLoS One,2021,16(6): e0253585. |
43 | Jin EH, Lee D, Bae JH, et al. Improved accuracy in optical diagnosis of colorectal polyps using convolutional neural networks with visual explanations[J].Gastroenterology,2020,158(8): 2169. |
44 | Takeda K, Kudo SE, Mori Y, et al. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy[J].Endoscopy,2017,49(8): 798. |
45 | Tokunaga M, Matsumura T, Nankinzan R, et al. Computer-aided diagnosis system using only white-light endoscopy for the prediction of invasion depth in colorectal cancer[J].Gastrointest Endosc,2021,93(3): 647. |
46 | Bai JW, Liu K, Gao L, et al. Computer-aided diagnosis in predicting the invasion depth of early colorectal cancer: a systematic review and meta-analysis of diagnostic test accuracy[J].Surg Endosc,2023,37(9): 6627. |
47 | Ozawa T, Ishihara S, Fujishiro M, et al. Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis[J].Gastrointest Endosc,2019,89(2): 416. |
48 | Stidham RW, Liu WS, Bishu S, et al. Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis[J].JAMA Netw Open,2019,2(5): e193963. |
49 | Mossotto E, Ashton JJ, Coelho T, et al. Classification of paediatric inflammatory bowel disease using machine learning[J].Sci Rep,2017,7(1): 2427. |
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