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Digestive Diseases and Sciences
Erschienen in: Digestive Diseases and Sciences 5/2022

21.06.2021 | Review

Artificial Intelligence in Endoscopy

verfasst von: Yutaka Okagawa, Seiichiro Abe, Masayoshi Yamada, Ichiro Oda, Yutaka Saito

Erschienen in: Digestive Diseases and Sciences | Ausgabe 5/2022

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Abstract

Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
Literatur
1.
2.
Bibault JE, Giraud P, Burgun A. Big data and machine learning in radiation oncology: State of the art and future prospects. Cancer Lett. 2016;382:110–117.PubMedCrossRef
3.
Gulshan V, Peng L, Coram M et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–2410.PubMedCrossRef
4.
5.
Yoshida H, Shimazu T, Kiyuna T et al. Automated histological classification of whole-slide images of gastric biopsy specimens. Gastric Cancer. 2018;21:249–257.PubMedCrossRef
6.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.PubMedCrossRef
7.
Kodashima S, Tanaka K, Matsuda K et al. First progress report on the Japan Endoscopy Database project. Dig Endosc. 2018;30:20–28.PubMedCrossRef
8.
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.PubMedCrossRef
9.
Dent J. Barrett’s esophagus: A historical perspective, an update on core practicalities and predictions on future evolutions of management. J Gastroenterol Hepatol. 2011;26:11–30.PubMedCrossRef
10.
Abrams JA, Kapel RC, Lindberg GM et al. Adherence to biopsy guidelines for Barrett’s esophagus surveillance in the community setting in the United States. Clin Gastroenterol Hepatol. 2009;7:736–742.PubMedPubMedCentralCrossRef
11.
Sharma P, Hawes RH, Bansal A et al. Standard endoscopy with random biopsies versus narrow band imaging targeted biopsies in Barrett’s oesophagus: a prospective, international, randomised controlled trial. Gut. 2013;62:15–21.PubMedCrossRef
12.
Sugimachi K, Ohno S, Matsuda H, Mori M, Matsuoka H, Kuwano H. Clinicopathologic study of early stage esophageal carcinoma. Surgery. 1989;105:706–710.PubMed
13.
Shimizu Y, Omori T, Yokoyama A et al. Endoscopic diagnosis of early squamous neoplasia of the esophagus with iodine staining: high-grade intra-epithelial neoplasia turns pink within a few minutes. J Gastroenterol Hepatol. 2008;23:546–550.PubMedCrossRef
14.
Muto M, Minashi K, Yano T et al. Early detection of superficial squamous cell carcinoma in the head and neck region and esophagus by narrow band imaging: a multicenter randomized controlled trial. J Clin Oncol. 2010;28:1566–1572.PubMedPubMedCentralCrossRef
15.
Ishihara R, Takeuchi Y, Chatani R et al. Prospective evaluation of narrow-band imaging endoscopy for screening of esophageal squamous mucosal high-grade neoplasia in experienced and less experienced endoscopists. Dis Esophagus. 2010;23:480–486.PubMedCrossRef
16.
van der Sommen F, Zinger S, Curvers WL et al. Computer-aided detection of early neoplastic lesions in Barrett’s esophagus. Endoscopy. 2016;48:617–624.PubMedCrossRef
17.
Swager AF, van der Sommen F, Klomp SR et al. Computer-aided detection of early Barrett’s neoplasia using volumetric laser endomicroscopy. Gastrointest Endosc. 2017;86:839–846.PubMedCrossRef
18.
Sehgal V, Rosenfeld A, Graham DG et al. Machine learning creates a simple endoscopic classification system that improves dysplasia detection in Barrett’s oesophagus amongst non-expert endoscopists. Gastroenterol Res Pract. 2018;2018:1872437.PubMedPubMedCentralCrossRef
19.
Ghatwary N, Zolgharni M, Ye X. Early esophageal adenocarcinoma detection using deep learning methods. Int J Comput Assist Radiol Surg. 2019;14:611–621.PubMedPubMedCentralCrossRef
20.
de Groof J, van der Sommen F, van der Putten J et al. The Argos project: The development of a computer-aided detection system to improve detection of Barrett’s neoplasia on white light endoscopy. United Eur Gastroenterol J. 2019;7:538–547.CrossRef
21.
de Groof AJ, Struyvenberg MR, van der Putten J et al. Deep-learning system detects neoplasia in patients with Barrett’s esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking. Gastroenterology. 2020;158:915-929.e4.PubMedCrossRef
22.
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). Gastrointest Endosc. 2020;91:1264-1271.e1.PubMedCrossRef
23.
Iwagami H, Ishihara R, Aoyama K et al. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol. 2021;36:131–136.PubMedCrossRef
24.
de Groof AJ, Struyvenberg MR, Fockens KN et al. Deep learning algorithm detection of Barrett’s neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video). Gastrointest Endosc. 2020;91:1242–1250.PubMedCrossRef
25.
Ebigbo A, Mendel R, Probst A et al. Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus. Gut. 2020;69:615–616.PubMedCrossRef
26.
Hong Jisu, Park Bo-Yong, Park Hyunjin. Convolutional neural network classifier for distinguishing Barrett’s esophagus and neoplasia endomicroscopy images. Conf Proc IEEE Eng Med Biol Soc. 2017;2017:2892–2895.
27.
28.
Horie Y, Yoshio T, Aoyama K et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc. 2019;89:25–32.PubMedCrossRef
29.
Cai SL, Li B, Tan WM et al. Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointest Endosc. 2019;90:745-753.e2.PubMedCrossRef
30.
Ohmori M, Ishihara R, Aoyama K et al. Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest Endosc. 2020;91:301-309.e1.PubMedCrossRef
31.
Liu G, Hua J, Wu Z et al. Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network. Ann Transl Med. 2020;8:486.PubMedPubMedCentralCrossRef
32.
Guo L, Xiao X, Wu C et al. Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointest Endosc. 2020;91:41–51.PubMedCrossRef
33.
Fukuda H, Ishihara R, Kato Y et al. Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video). Gastrointest Endosc. 2020;92:848–855.PubMedCrossRef
34.
35.
Kumagai Y, Takubo K, Kawada K et al. Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus. Esophagus. 2019;16:180–187.PubMedCrossRef
36.
Shin D, Protano MA, Polydorides AD et al. Quantitative analysis of high-resolution microendoscopic images for diagnosis of esophageal squamous cell carcinoma. Clin Gastroenterol Hepatol. 2015;13:272-279.e2.PubMedCrossRef
37.
Quang T, Schwarz RA, Dawsey SM et al. A tablet-interfaced high-resolution microendoscope with automated image interpretation for real-time evaluation of esophageal squamous cell neoplasia. Gastrointest Endosc. 2016;84:834–841.PubMedPubMedCentralCrossRef
38.
Everson M, Herrera L, Li W et al. Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study. United Eur Gastroenterol J. 2019;7:297–306.CrossRef
39.
Zhao YY, Xue DX, Wang YL et al. Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy. Endoscopy. 2019;51:333–341.PubMedCrossRef
40.
Nakagawa K, Ishihara R, Aoyama K et al. Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists. Gastrointest Endosc. 2019;90:407–414.PubMedCrossRef
41.
Tokai Y, Yoshio T, Aoyama K et al. Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma. Esophagus. 2020;17:250–256.PubMedCrossRef
42.
Shimamoto Y, Ishihara R, Kato Y et al. Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence. J Gastroenterol. 2020;55:1037–1045.PubMedCrossRef
43.
Uemura N, Okamoto S, Yamamoto S et al. Helicobacter pylori infection and the development of gastric cancer. N Engl J Med. 2001;345:784–789.PubMedCrossRef
44.
Kaji K, Hashiba A, Uotani C et al. Grading of atrophic gastritis is useful for risk stratification in endoscopic screening for gastric cancer. Am J Gastroenterol. 2019;114:71–79.PubMedCrossRef
45.
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. 2013;13:128.PubMedPubMedCentralCrossRef
46.
Nomura S, Terao S, Adachi K et al. Endoscopic diagnosis of gastric mucosal activity and inflammation. Dig Endosc. 2013;25:136–146.PubMedCrossRef
47.
Katai H, Ishikawa T, Akazawa K et al. Five-year survival analysis of surgically resected gastric cancer cases in Japan: A retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001–2007). Gastric Cancer. 2018;21:144–154.PubMedCrossRef
48.
Sano T, Coit DG, Kim HH et al. Proposal of a new stage grouping of gastric cancer for TNM classification: International Gastric Cancer Association staging project. Gastric Cancer. 2017;20:217–225.PubMedCrossRef
49.
50.
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). 2015;94:e384.PubMedPubMedCentralCrossRef
51.
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. 2017;25:106–111.PubMedPubMedCentralCrossRef
52.
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. 2019;54:158–163.PubMedCrossRef
53.
Itoh T, Kawahira H, Nakashima H, Yata N. Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images. Endosc Int Open. 2018;6:E139–E144.PubMedPubMedCentralCrossRef
54.
Nakashima H, Kawahira H, Kawachi H, Sakaki N. Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging-bright and linked color imaging: a single-center prospective study. Ann Gastroenterol. 2018;31:462–468.PubMedPubMedCentral
55.
Zheng W, Zhang X, Kim JJ et al. High accuracy of convolutional neural network for evaluation of helicobacter pylori infection based on endoscopic images: preliminary experience. Clin Transl Gastroenterol. 2019;10:e00109.PubMedPubMedCentralCrossRef
56.
Nakashima H, Kawahira H, Kawachi H, Sakaki N. Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: a single-center prospective study (with video). Gastric Cancer. 2020;23:1033–1040.PubMedCrossRef
57.
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. 2018;21:653–660.PubMedCrossRef
58.
Sakai Y, Takemoto S, Hori K et al. Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network. Conf Proc IEEE Eng Med Biol Soc. 2018;2018:4138–4141.
59.
Wu L, Zhou W, Wan X et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy. 2019;51:522–531.PubMedCrossRef
60.
Luo H, Xu G, Li C et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 2019;20:1645–1654.PubMedCrossRef
61.
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. 2020;62:103146.PubMedPubMedCentralCrossRef
62.
Miyaki R, Yoshida S, Tanaka S et al. Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement. J Gastroenterol Hepatol. 2013;28:841–847.PubMedCrossRef
63.
Miyaki R, Yoshida S, Tanaka S et al. A computer system to be used with laser-based endoscopy for quantitative diagnosis of early gastric cancer. J Clin Gastroenterol. 2015;49:108–115.PubMedCrossRef
64.
Lee JH, Kim YJ, Kim YW et al. Spotting malignancies from gastric endoscopic images using deep learning. Surg Endosc. 2019;33:3790–3797.PubMedCrossRef
65.
Li L, Chen Y, Shen Z et al. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer. 2020;23:126–132.PubMedCrossRef
66.
67.
Cho BJ, Bang CS, Park SW et al. Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network. Endoscopy. 2019;51:1121–1129.PubMedCrossRef
68.
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. 2020;92:856-865.e1.PubMedCrossRef
69.
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. 2020;52:1077–1083.PubMedCrossRef
70.
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. 2021;36:482–489.PubMedCrossRef
71.
72.
Kanesaka T, Lee TC, Uedo N et al. Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging. Gastrointest Endosc. 2018;87:1339–1344.PubMedCrossRef
73.
An P, Yang D, Wang J et al. A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy. Gastric Cancer. 2020;23:884–892.PubMedCrossRef
74.
75.
76.
Kubota K, Kuroda J, Yoshida M, Ohta K, Kitajima M. Medical image analysis: computer-aided diagnosis of gastric cancer invasion on endoscopic images. Surg Endosc. 2012;26:1485–1489.PubMedCrossRef
77.
Zhu Y, Wang QC, Xu MD et al. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc. 2019;89:806-815.e1.PubMedCrossRef
78.
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. 2019;8:1310.PubMedCentralCrossRef
79.
Cho BJ, Bang CS, Lee JJ, Seo CW, Kim JH. Prediction of submucosal invasion for gastric neoplasms in endoscopic images using deep-learning. J Clin Med. 2020;9:1858.PubMedCentralCrossRef
80.
Nagao S, Tsuji Y, Sakaguchi Y et al. Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging. Gastrointest Endosc. 2020;92:866-873.e1.PubMedCrossRef
81.
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. 2019;68:2161–2169.PubMedCrossRef
82.
83.
84.
Dokoutsidou H, Karagiannis S, Giannakoulopoulou E et al. A study comparing an endoscopy nurse and an endoscopy physician in capsule endoscopy interpretation. Eur J Gastroenterol Hepatol. 2011;23:166–170.PubMedCrossRef
85.
Pennazio M, Spada C, Eliakim R et al. Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Clinical Guideline. Endoscopy. 2015;47:352–376.PubMedCrossRef
86.
Pan G, Yan G, Qiu X, Cui J. Bleeding detection in wireless capsule endoscopy based on probabilistic neural network. J Med Syst. 2011;35:1477–1484.PubMedCrossRef
87.
Fu Y, Zhang W, Mandal M, Meng MQ. Computer-aided bleeding detection in WCE video. IEEE J Biomed Health Inform. 2014;18:636–642.PubMedCrossRef
88.
Hassan AR, Haque MA. Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos. Comput Methods Prog Biomed. 2015;122:341–353.CrossRef
89.
Jia X, Meng MQ. A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images. Conf Proc IEEE Eng Med Biol Soc 2016;2016:639–642.
90.
91.
Becq A, Rahmi G, Perrod G, Cellier C. Hemorrhagic angiodysplasia of the digestive tract: pathogenesis, diagnosis, and management. Gastrointest Endosc. 2017;86:792–806.PubMedCrossRef
92.
Leenhardt R, Vasseur P, Li C et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest Endosc. 2019;89:189–194.PubMedCrossRef
93.
Kumar R, Zhao Q, Seshamani S, Mullin G, Hager G, Dassopoulos T. Assessment of Crohn’s disease lesions in wireless capsule endoscopy images. IEEE Trans Biomed Eng. 2012;59:355–362.PubMedCrossRef
94.
Yuan Y, Wang J, Li B, Meng MQ. Saliency based ulcer detection for wireless capsule endoscopy diagnosis. IEEE Trans Med Imaging. 2015;34:2046–2057.PubMedCrossRef
95.
Charisis VS, Hadjileontiadis LJ. Potential of hybrid adaptive filtering in inflammatory lesion detection from capsule endoscopy images. World J Gastroenterol. 2016;22:8641–8657.PubMedPubMedCentralCrossRef
96.
Fan S, Xu L, Fan Y, Wei K, Li L. Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys Med Biol. 2018;63:165001.PubMedCrossRef
97.
Aoki T, Yamada A, Aoyama K et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc. 2019;89:357-363.e2.PubMedCrossRef
98.
Klang E, Barash Y, Margalit RY et al. Deep learning algorithms for automated detection of Crohn’s disease ulcers by video capsule endoscopy. Gastrointest Endosc. 2020;91:606-613.e2.PubMedCrossRef
99.
Wang S, Xing Y, Zhang L, Gao H, Zhang H. Deep convolutional neural network for ulcer recognition in wireless capsule endoscopy: experimental feasibility and optimization. Comput Math Methods Med. 2019;2019:7546215.PubMedPubMedCentralCrossRef
100.
Li B, Meng MQ, Lau JY. Computer-aided small bowel tumor detection for capsule endoscopy. Artif Intell Med. 2011;52:11–16.PubMedCrossRef
101.
Faghih Dinevari V, Karimian Khosroshahi G, Zolfy Lighvan M. Singular value decomposition based features for automatic tumor detection in wireless capsule endoscopy images. Appl Bionics Biomech. 2016;2016:3678913.PubMedPubMedCentralCrossRef
102.
Liu G, Yan G, Kuang S, Wang Y. Detection of small bowel tumor based on multi-scale curvelet analysis and fractal technology in capsule endoscopy. Comput Biol Med. 2016;70:131–138.PubMedCrossRef
103.
Constantinescu AF, Ionescu M, Iovănescu VF et al. A computer-aided diagnostic system for intestinal polyps identified by wireless capsule endoscopy. Rom J Morphol Embryol. 2016;57:979–984.PubMed
104.
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. Gastrointest Endosc. 2020;92:144-151.e1.PubMedCrossRef
105.
He JY, Wu X, Jiang YG, Peng Q, Jain R. Hookworm detection in wireless capsule endoscopy images with deep learning. IEEE Trans Image Process. 2018;27:2379–2392.PubMedCrossRef
106.
Aoki T, Yamada A, Aoyama K et al. Clinical usefulness of deep learning-based system as the first screening on small-bowel capsule endoscopy reading. Dig Endosc. 2020;32:585–591.PubMedCrossRef
107.
Park J, Hwang Y, Nam JH et al. Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading. PLoS One. 2020;15:0241474.CrossRef
108.
Ding Z, Shi H, Zhang H et al. Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology. 2019;157:1044-1054.e5.PubMedCrossRef
109.
Aoki T, Yamada A, Kato Y et al. Automatic detection of various abnormalities in capsule endoscopy videos by a deep learning-based system: a multicenter study. Gastrointest Endosc. 2021;93:165-173.e1.PubMedCrossRef
110.
Otani K, Nakada A, Kurose Y et al. Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network. Endoscopy. 2020;52:786–791.PubMedCrossRef
111.
112.
Ciaccio EJ, Tennyson CA, Bhagat G, Lewis SK, Green PH. Classification of videocapsule endoscopy image patterns: comparative analysis between patients with celiac disease and normal individuals. Biomed Eng 2010;9:44.
113.
Zhou T, Han G, Li BN et al. Quantitative analysis of patients with celiac disease by video capsule endoscopy: a deep learning method. Comput Biol Med. 2017;85:1–6.PubMedCrossRef
114.
Li B, Xu G, Zhou R, Wang T. Computer aided wireless capsule endoscopy video segmentation. Med Phys. 2015;42:645–652.PubMedCrossRef
115.
Brenner H, Chang-Claude J, Seiler CM, Rickert A, Hoffmeister M. Protection from colorectal cancer after colonoscopy: a population-based, case-control study. Ann Intern Med. 2011;154:22–30.PubMedCrossRef
116.
Zauber AG, Winawer SJ, O’Brien MJ et al. Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N Engl J Med. 2012;366:687–696.PubMedPubMedCentralCrossRef
117.
Kaminski MF, Regula J, Kraszewska E et al. Quality indicators for colonoscopy and the risk of interval cancer. N Engl J Med. 2010;362:1795–1803.PubMedCrossRef
118.
119.
Rex DK, Cutler CS, Lemmel GT et al. Colonoscopic miss rates of adenomas determined by back-to-back colonoscopies. Gastroenterology. 1997;112:24–28.PubMedCrossRef
120.
van Rijn JC, Reitsma JB, Stoker J, Bossuyt PM, van Deventer SJ, Dekker E. Polyp miss rate determined by tandem colonoscopy: a systematic review. Am J Gastroenterol. 2006;101:343–350.PubMedCrossRef
121.
Rex DK, Kahi C, O’Brien M et al. The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc. 2011;73:419–422.PubMedCrossRef
122.
Rees CJ, Rajasekhar PT, Wilson A et al. Narrow band imaging optical diagnosis of small colorectal polyps in routine clinical practice: the Detect Inspect Characterise Resect and Discard 2 (DISCARD 2) study. Gut. 2017;66:887–895.PubMedCrossRef
123.
Fernández-Esparrach G, Bernal J, López-Cerón M et al. Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. Endoscopy. 2016;48:837–842.PubMedCrossRef
124.
Billah M, Waheed S, Rahman MM. An automatic gastrointestinal polyp detection system in video endoscopy using fusion of color wavelet and convolutional neural network features. Int J Biomed Imaging. 2017;2017:9545920.PubMedPubMedCentralCrossRef
125.
Misawa M, Kudo SE, Mori Y et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology. 2018;154:2027-2029.e3.PubMedCrossRef
126.
Urban G, Tripathi P, Alkayali T et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 2018;155:1069-1078.e8.PubMedCrossRef
127.
Wang P, Xiao X, Glissen Brown JR et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng. 2018;2:741–748.PubMedCrossRef
128.
Zhang R, Zheng Y, Poon CCY, Shen D, Lau JYW. Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Pattern Recogn. 2018;83:209–219.CrossRef
129.
Yamada M, Saito Y, Imaoka H et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep. 2019;9:14465.PubMedPubMedCentralCrossRef
130.
Becq A, Chandnani M, Bharadwaj S et al. Effectiveness of a deep-learning polyp detection system in prospectively collected colonoscopy videos with variable bowel preparation quality. J Clin Gastroenterol. 2020;54:554–557.PubMedCrossRef
131.
Zhou G, Xiao X, Tu M et al. Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy. PLoS One. 2020;15:e0231880.PubMedPubMedCentralCrossRef
132.
Lee JY, Jeong J, Song EM et al. Real-time detection of colon polyps during colonoscopy using deep learning: systematic validation with four independent datasets. Sci Rep. 2020;10:8379.PubMedPubMedCentralCrossRef
133.
134.
Gao J, Guo Y, Sun Y, Qu G. Application of deep learning for early screening of colorectal precancerous lesions under white light endoscopy. Comput Math Methods Med. 2020;2020:8374317.PubMedPubMedCentralCrossRef
135.
Li T, Glissen Brown JR, Tsourides K, Mahmud N, Cohen JM, Berzin TM. Training a computer-aided polyp detection system to detect sessile serrated adenomas using public domain colonoscopy videos. Endosc Int Open. 2020;8:E1448–E1454.PubMedPubMedCentralCrossRef
136.
Klare P, Sander C, Prinzen M et al. Automated polyp detection in the colorectum: a prospective study (with videos). Gastrointest Endosc. 2019;89:576-582.e1.PubMedCrossRef
137.
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. 2019;68:1813–1819.PubMedCrossRef
138.
Liu WN, Zhang YY, Bian XQ, Wang LJ, Yang Q, Zhang XD, Huang J. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol. 2020;26:13–19.PubMedCrossRef
139.
Wang P, Liu X, Berzin TM et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020;5:343–351.PubMedCrossRef
140.
Gong D, Wu L, Zhang J et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol. 2020;5:352–361.PubMedCrossRef
141.
Repici A, Badalamenti M, Maselli R et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology. 2020;159:512-520.e7.PubMedCrossRef
142.
Wang P, Liu P, Glissen Brown JR et al. Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study. Gastroenterology. 2020;159:1252-1261.e5.PubMedCrossRef
143.
Lui TKL, Hui CKY, Tsui VWM et al. New insights on missed colonic lesions during colonoscopy through artificial intelligence-assisted real-time detection (with video). Gastrointest Endosc. 2021;93:193-200.e1.PubMedCrossRef
144.
145.
Mesejo P, Pizarro D, Abergel A et al. Computer-aided classification of gastrointestinal lesions in regular colonoscopy. IEEE Trans Med Imaging. 2016;35:2051–2063.PubMedCrossRef
146.
Komeda Y, Handa H, Watanabe T et al. Computer-aided diagnosis based on convolutional neural network system for colorectal polyp classification: preliminary experience. Oncology. 2017;93:30–34.PubMedCrossRef
147.
Sánchez-Montes C, Sánchez FJ, Bernal J et al. Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis. Endoscopy. 2019;51:261–265.PubMedCrossRef
148.
Yang YJ, Cho BJ, Lee MJ et al. Automated classification of colorectal neoplasms in white-light colonoscopy images via deep learning. J Clin Med. 2020;9:1593.PubMedCentralCrossRef
149.
150.
Min M, Su S, He W, Bi Y, Ma Z, Liu Y. Computer-aided diagnosis of colorectal polyps using linked color imaging colonoscopy to predict histology. Sci Rep. 2019;9:2881.PubMedPubMedCentralCrossRef
151.
Kuiper T, Alderlieste YA, Tytgat KM et al. Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence. Endoscopy. 2015;47:56–62.PubMed
152.
Rath T, Tontini GE, Vieth M, Nägel A, Neurath MF, Neumann H. In vivo real-time assessment of colorectal polyp histology using an optical biopsy forceps system based on laser-induced fluorescence spectroscopy. Endoscopy. 2016;48:557–562.PubMedCrossRef
153.
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. 2013;25:488–494.PubMedCrossRef
154.
Inomata H, Tamai N, Aihara H et al. Efficacy of a novel auto-fluorescence imaging system with computer-assisted color analysis for assessment of colorectal lesions. World J Gastroenterol. 2013;19:7146–7153.PubMedPubMedCentralCrossRef
155.
Horiuchi H, Tamai N, Kamba S, Inomata H, Ohya TR, Sumiyama K. Real-time computer-aided diagnosis of diminutive rectosigmoid polyps using an auto-fluorescence imaging system and novel color intensity analysis software. Scand J Gastroenterol. 2019;54:800–805.PubMedCrossRef
156.
Tischendorf JJ, Gross S, Winograd R et al. Computer-aided classification of colorectal polyps based on vascular patterns: a pilot study. Endoscopy. 2010;42:203–207.PubMedCrossRef
157.
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. 2010;72:1047–1051.PubMedCrossRef
158.
Takemura Y, Yoshida S, Tanaka S et al. Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video). Gastrointest Endosc. 2012;75:179–185.PubMedCrossRef
159.
Tamaki T, Yoshimuta J, Kawakami M et al. Computer-aided colorectal tumor classification in NBI endoscopy using local features. Med Image Anal. 2013;17:78–100.PubMedCrossRef
160.
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. 2016;83:643–649.PubMedCrossRef
161.
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. 2017;5:E690–E694.PubMedPubMedCentralCrossRef
162.
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. 2019;68:94–100.PubMedCrossRef
163.
Chen PJ, Lin MC, Lai MJ, Lin JC, Lu HH, Tseng VS. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology. 2018;154:568–575.PubMedCrossRef
164.
Song EM, Park B, Ha CA et al. Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model. Sci Rep. 2020;10:30.PubMedPubMedCentralCrossRef
165.
Zorron Cheng Tao PuL, Maicas G, Tian Y et al. Computer-aided diagnosis for characterization of colorectal lesions: comprehensive software that includes differentiation of serrated lesions. Gastrointest Endosc. 2020;92:891–899.CrossRef
166.
Rodriguez-Diaz E, Baffy G, Lo WK, Mashimo H, Vidyarthi G, Mohapatra SS, Singh SK. Real-time artificial intelligence-based histologic classification of colorectal polyps with augmented visualization. Gastrointest Endosc. 2021;93:662–670.PubMedCrossRef
167.
Mori Y, Kudo SE, Wakamura K et al. Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos). Gastrointest Endosc. 2015;81:621–629.PubMedCrossRef
168.
Mori Y, Kudo SE, Chiu PW et al. Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study. Endoscopy. 2016;48:1110–1118.PubMedCrossRef
169.
Misawa M, Kudo SE, Mori Y et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology. 2016;150:1531-1532.e3.PubMedCrossRef
170.
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. 2017;12:757–766.PubMedCrossRef
171.
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. 2018;169:357–366.PubMedCrossRef
172.
Kudo SE, Misawa M, Mori Y et al. Artificial intelligence-assisted system improves endoscopic identification of colorectal neoplasms. Clin Gastroenterol Hepatol. 2020;18:1874-1881.e2.PubMedCrossRef
173.
André B, Vercauteren T, Buchner AM, Krishna M, Ayache N, Wallace MB. Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps. World J Gastroenterol. 2012;18:5560–5569.PubMedPubMedCentralCrossRef
174.
Ştefănescu D, Streba C, Cârţână ET, Săftoiu A, Gruionu G, Gruionu LG. Computer aided diagnosis for confocal laser endomicroscopy in advanced colorectal adenocarcinoma. PLoS One. 2016;11:e0154863.PubMedPubMedCentralCrossRef
175.
Takeda K, Kudo SE, Mori Y et al. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy. Endoscopy. 2017;49:798–802.PubMedCrossRef
176.
Ito N, Kawahira H, Nakashima H, Uesato M, Miyauchi H, Matsubara H. Endoscopic diagnostic support system for cT1b colorectal cancer using deep learning. Oncology. 2019;96:44–50.PubMedCrossRef
177.
Lui TKL, Wong KKY, Mak LLY, Ko MKL, Tsao SKK, Leung WK. Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence. Endosc Int Open. 2019;7:E514–E520.PubMedPubMedCentralCrossRef
178.
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. Gastrointest Endosc. 2021;93:647–653.PubMedCrossRef
179.
Nakajima Y, Zhu X, Nemoto D et al. Diagnostic performance of artificial intelligence to identify deeply invasive colorectal cancer on non-magnified plain endoscopic images. Endosc Int Open. 2020;8:E1341–E1348.PubMedPubMedCentralCrossRef
180.
Maeda Y, Kudo SE, 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. 2019;89:408–415.PubMedCrossRef
181.
Quénéhervé L, David G, Bourreille A et al. Quantitative assessment of mucosal architecture using computer-based analysis of confocal laser endomicroscopy in inflammatory bowel diseases. Gastrointest Endosc. 2019;89:626–636.PubMedCrossRef
182.
Ozawa T, Ishihara S, Fujishiro M et al. Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis. Gastrointest Endosc. 2019;89:416-421.e1.PubMedCrossRef
183.
Stidham RW, Liu W, Bishu S et al. Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. JAMA Netw Open. 2019;2:193963.CrossRef
184.
Bossuyt P, Nakase H, Vermeire S, et al. Automatic, computer-aided determination of endoscopic and histological inflammation in patients with mild to moderate ulcerative colitis based on red density. Gut. 2020.
185.
Takenaka K, Ohtsuka K, Fujii T et al. Development and validation of a deep neural network for accurate evaluation of endoscopic images from patients with ulcerative colitis. Gastroenterology. 2020;158:2150–2157.PubMedCrossRef
186.
Tsuboi A, Oka S, Aoyama K et al. Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images. Dig Endosc. 2020;32:382–390.PubMedCrossRef
Metadaten
Titel
Artificial Intelligence in Endoscopy
verfasst von
Yutaka Okagawa
Seiichiro Abe
Masayoshi Yamada
Ichiro Oda
Yutaka Saito
Publikationsdatum
21.06.2021
Verlag
Springer US
Erschienen in
Digestive Diseases and Sciences / Ausgabe 5/2022
Print ISSN: 0163-2116
Elektronische ISSN: 1573-2568
DOI
https://doi.org/10.1007/s10620-021-07086-z

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