Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement

Helmut Messmann; Raf Bisschops; Giulio Antonelli; Diogo Libânio; Pieter Sinonquel; Mohamed Abdelrahim; Omer F. Ahmad; Miguel Areia; Jacques J. G. H. M. Bergman; Pradeep Bhandari; Ivo Boskoski; Evelien Dekker; Dirk Domagk; Alanna Ebigbo; Tom Eelbode; Rami Eliakim; Michael Häfner; Rehan J. Haidry; Rodrigo Jover; Michal F. Kaminski; Roman Kuvaev; Yuichi Mori; Maxime Palazzo; Alessandro Repici; Emanuele Rondonotti; Matthew D. Rutter; Yutaka Saito; Prateek Sharma; Cristiano Spada; Marco Spadaccini; Andrew Veitch; Ian M. Gralnek; Cesare Hassan; Mario Dinis-Ribeiro

doi:10.1055/a-1950-5694

Endoscopy, Table of Contents

Endoscopy 2022; 54(12): 1211-1231
DOI: 10.1055/a-1950-5694

Position Statement

Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement

Authors

Helmut Messmann‡

¹III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
Raf Bisschops ‡

²Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
Giulio Antonelli

³Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy

⁴Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Italy
Diogo Libânio

⁵Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal

⁶MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
Pieter Sinonquel

²Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
Mohamed Abdelrahim

⁷Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
Omer F. Ahmad

⁸Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK

⁹Division of Surgery and Interventional Sciences, University College London Hospital, London, UK

¹⁰Gastrointestinal Services, University College London Hospital, London, UK
Miguel Areia

¹¹Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal
Jacques J. G. H. M. Bergman

¹²Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
Pradeep Bhandari

⁷Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
Ivo Boskoski

¹³Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
Evelien Dekker

¹²Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
Dirk Domagk

¹⁴Department of Medicine I, Josephs-Hospital Warendorf, Academic Teaching Hospital, University of Muenster, Warendorf, Germany
Alanna Ebigbo

¹III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
Tom Eelbode

¹⁵Department of Electrical Engineering (ESAT/PSI), Medical Imaging Research Center, KU Leuven, Leuven, Belgium
Rami Eliakim

¹⁶Department of Gastroenterology, Sheba Medical Center Tel Hashomer & Sackler School of Medicine, Tel-Aviv University, Ramat Gan, Israel
Michael Häfner

¹⁷2nd Medical Department, Barmherzige Schwestern Krankenhaus, Vienna, Austria
Rehan J. Haidry

⁸Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK

⁹Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
Rodrigo Jover

¹⁸Servicio de Gastroenterología, Hospital General Universitario Dr. Balmis, Instituto de Investigación Biomédica de Alicante ISABIAL, Departamento de Medicina Clínica, Universidad Miguel Hernández, Alicante, Spain
Michal F. Kaminski

¹⁹Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway

²⁰Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland

²¹Department of Oncological Gastroenterology and Department of Cancer Prevention, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
Roman Kuvaev^**

²²Endoscopy Department, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation

²³Department of Gastroenterology, Faculty of Additional Professional Education, N.A. Pirogov Russian National Research Medical University, Moscow, Russian Federation
Yuichi Mori

¹⁹Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway

²⁴Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
Maxime Palazzo

²⁵European Hospital, Marseille, France
Alessandro Repici

²⁶Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy

²⁷IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
Emanuele Rondonotti

²⁸Gastroenterology Unit, Valduce Hospital, Como, Italy
Matthew D. Rutter

²⁹North Tees and Hartlepool NHS Foundation Trust, Stockton-on-Tees, UK

³⁰Population Health Sciences Institute, Newcastle University, Newcastle, UK
Yutaka Saito

³¹Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
Prateek Sharma

³²Gastroenterology and Hepatology Division, University of Kansas School of Medicine, Kansas, USA

³³Kansas City VA Medical Center, Kansas City, USA
Cristiano Spada

¹³Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy

³⁴Digestive Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
Marco Spadaccini

²⁶Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy

²⁷IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
Andrew Veitch

³⁵Department of Gastroenterology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UK
Ian M. Gralnek

³⁶Ellen and Pinchas Mamber Institute of Gastroenterology and Hepatology, Emek Medical Center, Afula, Israel

³⁷Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
Cesare Hassan^***

²⁶Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy

²⁷IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
Mario Dinis-Ribeiro^***

⁵Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal

Abstract

This ESGE Position Statement defines the expected value of artificial intelligence (AI) for the diagnosis and management of gastrointestinal neoplasia within the framework of the performance measures already defined by ESGE. This is based on the clinical relevance of the expected task and the preliminary evidence regarding artificial intelligence in artificial or clinical settings.

Main recommendations: (1) For acceptance of AI in assessment of completeness of upper GI endoscopy, the adequate level of mucosal inspection with AI should be comparable to that assessed by experienced endoscopists. (2) For acceptance of AI in assessment of completeness of upper GI endoscopy, automated recognition and photodocumentation of relevant anatomical landmarks should be obtained in ≥90% of the procedures. (3) For acceptance of AI in the detection of Barrett’s high grade intraepithelial neoplasia or cancer, the AI-assisted detection rate for suspicious lesions for targeted biopsies should be comparable to that of experienced endoscopists with or without advanced imaging techniques. (4) For acceptance of AI in the management of Barrett’s neoplasia, AI-assisted selection of lesions amenable to endoscopic resection should be comparable to that of experienced endoscopists. (5) For acceptance of AI in the diagnosis of gastric precancerous conditions, AI-assisted diagnosis of atrophy and intestinal metaplasia should be comparable to that provided by the established biopsy protocol, including the estimation of extent, and consequent allocation to the correct endoscopic surveillance interval. (6) For acceptance of artificial intelligence for automated lesion detection in small-bowel capsule endoscopy (SBCE), the performance of AI-assisted reading should be comparable to that of experienced endoscopists for lesion detection, without increasing but possibly reducing the reading time of the operator. (7) For acceptance of AI in the detection of colorectal polyps, the AI-assisted adenoma detection rate should be comparable to that of experienced endoscopists. (8) For acceptance of AI optical diagnosis (computer-aided diagnosis [CADx]) of diminutive polyps (≤5 mm), AI-assisted characterization should match performance standards for implementing resect-and-discard and diagnose-and-leave strategies. (9) For acceptance of AI in the management of polyps ≥ 6 mm, AI-assisted characterization should be comparable to that of experienced endoscopists in selecting lesions amenable to endoscopic resection.

Full Text

References

References
1 Bisschops R, East JE, Hassan C. et al. Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) Guideline – Update 2019. Endoscopy 2019; 51: 1155-1179
2 Ahmad OF, Stoyanov D, Lovat LB. Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues. Tech Gastrointest Endosc 2019; 150636
3 Byrne M. Artificial intelligence in gastroenterology. Tech Gastrointest Endosc 2019; 150641
4 de Groen PC. Using artificial intelligence to improve adequacy of inspection in gastrointestinal endoscopy. Tech Gastrointest Endosc 2019; 150640
5 Parasa S, Wallace M, Bagci U. et al. Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit. Gastrointest Endosc 2020;
6 Mori Y, Kudo S, Misawa M. et al. Artificial intelligence for colorectal polyp detection and characterization. Curr Treat Options Gastroenterol 2020;
7 van der Sommen F, de Groof J, Struyvenberg M. et al. Machine learning in GI endoscopy: practical guidance in how to interpret a novel field. Gut 2020;
8 Bisschops R, Areia M, Coron E. et al. Performance measures for upper gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy 2016; 48: 843-864
9 Kaminski MF, Thomas-Gibson S, Bugajski M. et al. Performance measures for lower gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy 2017; 49: 378-397
10 Domagk D, Oppong KW, Aabakken L. et al. Performance measures for ERCP and endoscopic ultrasound: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy 2018; 50: 1116-1127
11 Rutter MD, Senore C, Bisschops R. et al. The European Society of Gastrointestinal Endoscopy Quality Improvement Initiative: developing performance measures. Endoscopy 2016; 48: 81-89
12 Menon S, Trudgill N. How commonly is upper gastrointestinal cancer missed at endoscopy?. A meta-analysis. Endosc Int Open 2014; 2: E46-50
13 Pimenta-Melo AR, Monteiro-Soares M, Libânio D. et al. Missing rate for gastric cancer during upper gastrointestinal endoscopy: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol 2016; 28: 1041-1049
14 Frazzoni L, Arribas J, Antonelli G. et al. Endoscopists’ diagnostic accuracy in detecting upper gastrointestinal neoplasia in the framework of artificial intelligence studies. Endoscopy 2021;
15 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
16 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 2020; 91: 332-339.e3
17 Choi SJ, Khan MA, Choi HS. et al. Development of artificial intelligence system for quality control of photo documentation in esophagogastroduodenoscopy. Surg Endosc 2022; 36: 57-65
18 Li Y-D, Zhu S-W, Yu J-P. et al. Intelligent detection endoscopic assistant: An artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time. Dig Liver Dis 2021; 53: 216-223
19 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
20 Xu Z, Tao Y, Wenfang Z. et al. Upper gastrointestinal anatomy detection with multi-task convolutional neural networks. Healthc Technol Lett 2019; 6: 176-180
21 Rodríguez de Santiago E, Hernanz N, Marcos-Prieto HM. et al. Rate of missed oesophageal cancer at routine endoscopy and survival outcomes: A multicentric cohort study. United Eur Gastroenterol J 2019; 7: 189-198
22 Buchner AM, Shahid MW, Heckman MG. et al. Trainee participation is associated with increased small adenoma detection. Gastrointest Endosc 2011; 73: 1223-1231
23 Aslanian HR, Shieh FK, Chan FW. et al. Nurse observation during colonoscopy increases polyp detection: a randomized prospective study. Am J Gastroenterol 2013; 108: 166-172
24 Lee CK, Park DI, Lee S-H. et al. Participation by experienced endoscopy nurses increases the detection rate of colon polyps during a screening colonoscopy: a multicenter, prospective, randomized study. Gastrointest Endosc 2011; 74: 1094-1102
25 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
26 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
27 Shin D, Protano M-A, 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
28 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;
29 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
30 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
31 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
32 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
33 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
34 Arribas J, Antonelli G, Frazzoni L. et al. Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis. Gut 2020;
35 Yu T, Geng J, Song W. et al. Diagnostic accuracy of magnifying endoscopy with narrow band imaging and its diagnostic value for invasion depth staging in esophageal squamous cell carcinoma: A systematic review and meta-analysis. BioMed Res Int 2018; 2018: 8591387
36 Inoue H, Kaga M, Ikeda H. et al. Magnification endoscopy in esophageal squamous cell carcinoma: a review of the intrapapillary capillary loop classification. Ann Gastroenterol 2015; 28: 41-48
37 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
38 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
39 Visrodia K, Singh S, Krishnamoorthi R. et al. Magnitude of missed esophageal adenocarcinoma after Barrett’s esophagus diagnosis: A systematic review and meta-analysis. Gastroenterology 2016; 150: 599-607.e7 quiz e14–15
40 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
41 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; S0016510720300262
42 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; S0016510720300146
43 Lui TKL, Tsui VWM, Leung WK. Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis. Gastrointest Endosc 2020; 92: 821-830.e9
44 Sharma P, Shaheen NJ, Katzka D. et al. AGA clinical practice update on endoscopic treatment of Barrett’s esophagus with dysplasia and/or early cancer: Expert review. Gastroenterology 2020; 158: 760-769
45 Wagner A, Zandanell S, Kiesslich T. et al. Systematic review on optical diagnosis of early gastrointestinal neoplasia. J Clin Med 2021; 10: 2794
46 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
47 Ebigbo A, Mendel R, Rückert T. et al. Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of artificial intelligence: a pilot study. Endoscopy 2021; 53: 878-883
48 Pimentel-Nunes P, Libânio D, Marcos-Pinto R. et al. Management of epithelial precancerous conditions and lesions in the stomach (MAPS II): European Society of Gastrointestinal Endoscopy (ESGE), European Helicobacter and Microbiota Study Group (EHMSG), European Society of Pathology (ESP), and Sociedade Portuguesa de Endoscopia Digestiva (SPED) guideline update 2019. Endoscopy 2019; 51: 365-388
49 Areia M, Carvalho R, Cadime AT. et al. Screening for gastric cancer and surveillance of premalignant lesions: a systematic review of cost-effectiveness studies. Helicobacter 2013; 18: 325-337
50 Areia M, Spaander MC, Kuipers EJ. et al. Endoscopic screening for gastric cancer: A cost-utility analysis for countries with an intermediate gastric cancer risk. United Eur Gastroenterol J 2018; 6: 192-202
51 Zhou HJ, Dan YY, Naidoo N. et al. A cost-effectiveness analysis evaluating endoscopic surveillance for gastric cancer for populations with low to intermediate risk. PloS One 2013; 8: e83959
52 Rugge M, Genta RM, Fassan M. et al. OLGA gastritis staging for the prediction of gastric cancer risk: A long-term follow-up study of 7436 patients. Am J Gastroenterol 2018; 113: 1621-1628
53 Lee JWJ, Zhu F, Srivastava S. et al. Severity of gastric intestinal metaplasia predicts the risk of gastric cancer: a prospective multicentre cohort study (GCEP). Gut 2022; 71: 854-863
54 Esposito G, Pimentel-Nunes P, Angeletti S. et al. Endoscopic grading of gastric intestinal metaplasia (EGGIM): a multicenter validation study. Endoscopy 2019; 51: 515-521
55 Guimarães P, Keller A, Fehlmann T. et al. Deep-learning based detection of gastric precancerous conditions. Gut 2020; 69: 4-6
56 Pimentel-Nunes P, Libânio D, Bastiaansen BAJ. et al. Endoscopic submucosal dissection for superficial gastrointestinal lesions: European Society of Gastrointestinal Endoscopy (ESGE) Guideline – Update 2022. Endoscopy 2022; 54: 591-622
57 Gotoda T, Yanagisawa A, Sasako M. et al. Incidence of lymph node metastasis from early gastric cancer: estimation with a large number of cases at two large centers. Gastric Cancer 2000; 3: 219-225
58 Kim EH, Park JC, Song IJ. et al. Prediction model for non-curative resection of endoscopic submucosal dissection in patients with early gastric cancer. Gastrointest Endosc 2017; 85: 976-983
59 Ponte A, Pinho R, Rodrigues A. et al. Review of small-bowel cleansing scales in capsule endoscopy: A panoply of choices. World J Gastrointest Endosc 2016; 8: 600-609
60 Nam JH, Oh DJ, Lee S. et al. Development and verification of a deep learning algorithm to evaluate small-bowel preparation quality. Diagn Basel Switz 2021; 11: 1127
61 Nam JH, Hwang Y, Oh DJ. et al. Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy. Sci Rep 2021; 11: 4417
62 Abou AliE, Histace A, Camus M. et al. Development and validation of a computed assessment of cleansing score for evaluation of quality of small-bowel visualization in capsule endoscopy. Endosc Int Open 2018; 6: E646-E651
63 Klein A, Gizbar M, Bourke MJ. et al. Validated computed cleansing score for video capsule endoscopy. Dig Endosc Off J Jpn Gastroenterol Endosc Soc 2016; 28: 564-569
64 Leenhardt R, Souchaud M, Houist G. et al. A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy. Endoscopy 2021; 53: 932-936
65 Noorda R, Nevárez A, Colomer A. et al. Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture. Sci Rep 2020; 10: 17706
66 Spada C, McNamara D, Despott EJ. et al. Performance measures for small-bowel endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy 2019; 51: 574-598
67 Dimas G, Spyrou E, Iakovidis DK. et al. Intelligent visual localization of wireless capsule endoscopes enhanced by color information. Comput Biol Med 2017; 89: 429-440
68 Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis 2004; 60: 91-110
69 Enns RA, Hookey L, Armstrong D. et al. Clinical practice guidelines for the use of video capsule endoscopy. Gastroenterology 2017; 152: 497-514
70 Sidhu R, Chetcuti ZammitS, Baltes P. et al. Curriculum for small-bowel capsule endoscopy and device-assisted enteroscopy training in Europe: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2020; 52: 669-686
71 Soffer S, Klang E, Shimon O. et al. Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. Gastrointest Endosc 2020; 92: 831-839.e8
72 Dray X, Iakovidis D, Houdeville C. et al. Artificial intelligence in small bowel capsule endoscopy – current status, challenges and future promise. J Gastroenterol Hepatol 2021; 36: 12-19
73 Qin K, Li J, Fang Y. et al. Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis. Surg Endosc 2022; 36: 16-31
74 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
75 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
76 Mascarenhas Saraiva MJ, Afonso J, Ribeiro T. et al. Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network. BMJ Open Gastroenterol 2021; 8: e000753
77 Piccirelli S, Milluzzo SM, Bizzotto A. et al. Small bowel capsule endoscopy and artificial intelligence: First or second reader?. Best Pract Res Clin Gastroenterol 2021; 52–53: 101742
78 Parmar R, Martel M, Rostom A. et al. Validated scales for colon cleansing: A systematic review. Am J Gastroenterol 2016; 111: 197-204 quiz 205
79 Zhou J, Wu L, Wan X. et al. A novel artificial intelligence system for the assessment of bowel preparation (with video). Gastrointest Endosc 2020; 91: 428-435.e2
80 Lai EJ, Calderwood AH, Doros G. et al. The Boston bowel preparation scale: a valid and reliable instrument for colonoscopy-oriented research. Gastrointest Endosc 2009; 69: 620-625
81 Yao L, Zhang L, Liu J. et al. Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: a four-group parallel study. Endoscopy 2021;
82 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
83 Lee RH, Tang RS, Muthusamy VR. et al. Quality of colonoscopy withdrawal technique and variability in adenoma detection rates (with videos). Gastrointest Endosc 2011; 74: 128-134
84 Corley DA, Jensen CD, Marks AR. et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 2014; 370: 1298-1306
85 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
86 Kaminski MF, Thomas-Gibson S, Bugajski M. et al. Performance measures for lower gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy 2017; 49: 378-397
87 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
88 Hassan C, Wallace MB, Sharma P. et al. New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection. Gut 2020; 69: 799-800
89 Misawa M, Kudo S-E, Mori Y. et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 2018; 154: 2027-2029.e3
90 Hassan C, Badalamenti M, Maselli R. et al. Computer-aided detection-assisted colonoscopy: classification and relevance of false positives. Gastrointest Endosc 2020;
91 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
92 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
93 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
94 Liu W-N, Zhang Y-Y, Bian X-Q. et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol 2020; 26: 13
95 Su J-R, Li Z, Shao X-J. et al. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc 2020; 91: 415-424.e4
96 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;
97 Wallace MB, Sharma P, Bhandari P. et al. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology 2022; 295-304
98 Kamba S, Tamai N, Saitoh I. et al. Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial. J Gastroenterol 2021; 56: 746-757
99 Glissen Brown JR, Mansour NM, Wang P. et al. Deep learning computer-aided polyp detection reduces adenoma miss rate: a United States multi-center randomized tandem colonoscopy study (CADeT-CS Trial). Clin Gastroenterol Hepatol 2022; 20: 1499-1507.e4
100 Repici A, Spadaccini M, Antonelli G. et al. Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut 2021;
101 Hassan C, Spadaccini M, Iannone A. et al. Performance of artificial intelligence for colonoscopy regarding adenoma and polyp detection: a meta-analysis. Gastrointest Endosc 2020;
102 Barua I, Vinsard D, Jodal H. et al. Artificial intelligence for polyp detection during colonoscopy: A systematic review and meta-analysis. Endoscopy 2020;
103 Spadaccini M, Hassan C, Alfarone L. et al. Comparing the number and relevance of false activations between 2 artificial intelligence computer-aided detection systems: the NOISE study. Gastrointest Endosc 2022; 95: 975-981.e1
104 Gupta N, Bansal A, Rao D. et al. Prevalence of advanced histological features in diminutive and small colon polyps. Gastrointest Endosc 2012; 75: 1022-1030
105 Greuter MJE, de Klerk CM, Meijer GA. et al. Screening for colorectal cancer with fecal immunochemical testing with and without postpolypectomy surveillance colonoscopy: A cost-effectiveness analysis. Ann Intern Med 2017; 167: 544-554
106 Krzeczewski B, Hassan C, Krzeczewska O. et al. Cost-effectiveness of colonoscopy in an organized screening program. Pol Arch Intern Med 2021; 131: 128-135
107 Lansdorp-Vogelaar I, Knudsen A, Brenner H. Cost-effectiveness of colorectal cancer screening – an overview. Best Pract Res Clin Gastroenterol 2010; 24: 439-449
108 Gordon IO, Sherman JD, Leapman M. et al. Life cycle greenhouse gas emissions of gastrointestinal biopsies in a surgical pathology laboratory. Am J Clin Pathol 2021; 156: 540-549.1
109 Mori Y, Kudo S-E, East JE. et al. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Gastrointest Endosc 2020; 92: 905-911.e1
110 Hassan C, Pickhardt PJ, Rex DK. A resect and discard strategy would improve cost-effectiveness of colorectal cancer screening. Clin Gastroenterol Hepatol 2010; 8: 865-869.e3
111 Vu HT, Sayuk GS, Hollander TG. et al. Resect and discard approach to colon polyps: real-world applicability among academic and community gastroenterologists. Dig Dis Sci 2015; 60: 502-508
112 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
113 Houwen BBSL, Hassan C, Coupé VMH. et al. Definition of competence standards for optical diagnosis of diminutive colorectal polyps: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54: 88-99
114 Mori Y, Kudo S-E, 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
115 Barua I, Wieszczy P, Kudo S. et al. Real-time artificial intelligence–based optical diagnosis of neoplastic polyps during colonoscopy. NEJM Evid 2022; 1
116 Hassan C, Balsamo G, Lorenzetti R. et al. Artificial intelligence allows leaving-in-situ colorectal polyps. Clin Gastroenterol Hepatol 2022; 11
117 Rondonotti E, Hassan C, Tamanini G. et al. Artificial intelligence assisted optical diagnosis for resect and discard strategy in clinical practice (Artificial intelligence BLI Characterization; ABC study). Endoscopy 2022;
118 Burgess NG, Hourigan LF, Zanati SA. et al. Risk stratification for covert invasive cancer among patients referred for colonic endoscopic mucosal resection: A large multicenter cohort. Gastroenterology 2017; 153: 732-742