Barrett’s esophagus is the only known precursor for esophageal adenocarcinoma. The stepwise progression of nondysplastic Barrett’s to low-grade dysplasia, high-grade dysplasia, intramucosal carcinoma, and invasive cancer provides an opportunity for endoscopic surveillance, with the goal to detect and effectively treat early neoplastic changes endoscopically, in order to avoid surgery and chemoradiation. Multiple limitations in the current endoscopic surveillance practice have led to missed early neoplasia and therefore reduced the effectiveness of the current surveillance practice. Artificial intelligence (AI)-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current endoscopic surveillance practice for Barrett’s esophagus, its limitations, the potential role of AI to improve these limitations, and our suggested framework to integrate AI into Barrett’s clinical practice.
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References
1.
ShaheenNJ, FalkGW, IyerPG, et al.Diagnosis and management of Barrett’s esophagus: An updated ACG guideline. Am J Gastroenterol, 2022; 117(4):559–587; doi: 10.1038/ajg.2015.322
2.
ThriftAP. Barrett’s esophagus and esophageal adenocarcinoma: How common are they really? Dig Dis Sci, 2018; 63(8):1988–1996; doi: 10.1007/s10620-018-5068-6
3.
QumseyaB, YangS, GuoY. Trends in prevalence of esophageal adenocarcinoma: Findings from a statewide database of over 6 million patients. Endosc Int Open, 2024; 12(2):E218–E226; doi: 10.1055/a-2221-7974
4.
SheikhM, RoshandelG, McCormackV, et al.Current status and future prospects for esophageal cancer. Cancers (Basel), 2023; 15(3):765; doi: 10.3390/cancers15030765
5.
WeustenB, BisschopsR, CoronE, et al.Endoscopic management of Barrett’s esophagus: European Society of Gastrointestinal Endoscopy (ESGE) position statement. Endoscopy, 2017; 49(2):191–198; doi: 10.1055/a-2176-2440
6.
FitzgeraldRC, Di PietroM, RagunathK, et al.;British Society of Gastroenterology. British society of gastroenterology guidelines on the diagnosis and management of Barrett’s oesophagus. Gut, 2014; 63(1):7–42; doi: gutjnl-2013-305372
7.
QumseyaB, SultanS, BainP, et al.;ASGE Standards of Practice Committee Chair. ASGE guideline on screening and surveillance of Barrett’s esophagus. Gastrointest Endosc, 2019; 90(3):335–359.e2; doi: 10.1016/j.gie.2019.05.012
8.
WaniS, YadlapatiR, SinghS, et al.;Post-Endoscopy Esophageal Neoplasia Expert Consensus Panel. Post-endoscopy esophageal neoplasia in Barrett’s esophagus: Consensus statements from an international expert panel. Gastroenterology, 2022; 162(2):366–372; doi: 10.1053/j.gastro.2021.09.067
9.
GuptaN, GaddamS, WaniSB, et al.Longer inspection time is associated with increased detection of high-grade dysplasia and esophageal adenocarcinoma in Barrett’s esophagus. Gastrointest Endosc, 2012; 76(3):531–538; doi: 10.1016/j.gie.2012.04.470
10.
GillRS, SinghR. Endoscopic imaging in Barrett’s esophagus: Current practice and future applications. Ann Gastroenterol, 2012; 25(2):89.
11.
PelegN, OllechJE, ShamahS, et al.Seattle protocol is more effective in detection of dysplasia compared to technology-assisted targeted biopsies in patients with Barrett’s esophagus. J Clin Med, 2023; 12(7):2544; doi: 10.3390/jcm12072544
12.
WaterhouseDJ, JanuszewiczW, AliS, et al.Spectral endoscopy enhances contrast for neoplasia in surveillance of Barrett’s esophagus. Cancer Res, 2021; 81(12):3415–3425; doi: 10.1158/0008-5472.CAN-21-0474
13.
MoriartyJP, ShahND, RubensteinJH, et al.Costs associated with Barrett’s esophagus screening in the community: An economic analysis of a prospective randomized controlled trial of sedated versus hospital unsedated versus mobile community unsedated endoscopy. Gastrointest Endosc, 2018; 87(1):88–94.e2; doi: 10.1016/j.gie.2017.04.019
14.
F van derS, ZingerS, CurversWL, et al.Computer-aided detection of early neoplastic lesions in Barrett’s esophagus. Endoscopy, 2016; 48(7):617–624; doi: 10.1055/s-0042-105284
15.
VisrodiaK, SinghS, KrishnamoorthiR, et al.Magnitude of missed esophageal adenocarcinoma after Barrett’s esophagus diagnosis: A systematic review and meta-analysis. Gastroenterology, 2016; 150(3):599–607.e7; doi: 10.1053/j.gastro.2015.11.040
16.
SawasT, MajzoubAM, HaddadJ, et al.;Post-Endoscopy Esophageal Adenocarcinoma Consensus Panel. Magnitude and time-trend analysis of postendoscopy esophageal adenocarcinoma: A systematic review and meta-analysis. Clin Gastroenterol Hepatol, 2022; 20(2):e31–50–e50; doi: 10.1016/j.cgh.2021.04.032
17.
PechO, GossnerL, MannerH, et al.Prospective evaluation of the macroscopic types and location of early Barrett’s neoplasia in 380 lesions. Endoscopy, 2007; 39(7):588–593; doi: 10.1055/s-2007-966363
18.
AbelaJE, GoingJJ, MackenzieJF, et al.Systematic four-quadrant biopsy detects Barrett’s dysplasia in more patients than nonsystematic biopsy. Am J Gastroenterol, 2008; 103(4):850–855; doi: 10.1111/j.1572-0241.2007.01746.x
19.
AbramsJA, KapelRC, LindbergGM, et al.Adherence to biopsy guidelines for Barrett’s esophagus surveillance in the community setting in the United States. Clin Gastroenterol Hepatol, 2009; 7(7):736–742; quiz 710; doi: 10.1016/j.cgh.2008.12.027
20.
CurversWL, ten KateFJ, KrishnadathKK, et al.Low-grade dysplasia in Barrett’s esophagus: Overdiagnosed and underestimated. Am J Gastroenterol, 2010; 105(7):1523–1530; doi: 10.1038/ajg.2010.171
21.
WaniS, QumseyaB, SultanS, et al.;Standards of Practice Committee. Endoscopic eradication therapy for patients with Barrett’s esophagus–associated dysplasia and intramucosal cancer. Gastrointest Endosc, 2018; 87(4):907–931.e9; doi: 10.1016/j.gie.2017.10.011
22.
PechO, MayA, MannerH, et al.Long-term efficacy and safety of endoscopic resection for patients with mucosal adenocarcinoma of the esophagus. Gastroenterology, 2014; 146(3):652–660.e1; doi: 10.1053/j.gastro.2013.11.006
23.
LeeL, RonellenfitschU, HofstetterWL, et al.Predicting lymph node metastases in early esophageal adenocarcinoma using a simple scoring system. J Am Coll Surg, 2013; 217(2):191–199; doi: 10.1016/j.jamcollsurg.2013.03.015
24.
MannerH, PechO, HeldmannY, et al.Efficacy, safety, and long-term results of endoscopic treatment for early stage adenocarcinoma of the esophagus with low-risk sm1 invasion. Clin Gastroenterol Hepatol, 2013; 11(6):630–635; quiz e45; doi: 10.1016/j.cgh.2012.12.040
25.
Pimentel-NunesP, Dinis-RibeiroM, PonchonT, et al.Endoscopic submucosal dissection: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Endoscopy, 2015; 47(9):829–854; doi: 10.1055/s-0034-1392882
26.
DraganovPV, WangAY, OthmanMO, et al.AGA institute clinical practice update: Endoscopic submucosal dissection in the United States. Clin Gastroenterol Hepatol, 2019; 17(1):16–25.e1; doi: 10.1016/j.cgh.2018.07.041
27.
MalikP, PathaniaM, RathaurVK, et al.Overview of artificial intelligence in medicine. J Family Med Prim Care, 2019; 8(7):2328–2331; doi: 10.4103/jfmpc.jfmpc_440_19
JuarezJM, MarcosM, StiglicG, et al.Artificial intelligence in medicine: 21st international conference on artificial intelligence in medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings [Internet]. Cham: Springer Nature Switzerland; 2023 [December 7, 2023]. (Lecture Notes in Computer Science; vol. 13897). https://link.springer.com/10.1007/978-3-031-34344-5
30.
TischendorfJJW, GrossS, WinogradR, et al.Computer-aided classification of colorectal polyps based on vascular patterns: A pilot study. Endoscopy, 2010; 42(3):203–207; doi: 10.1055/s-0029-1243861
31.
KominamiY, YoshidaS, TanakaS, 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(3):643–649; doi: 10.1016/j.gie.2015.08.004
32.
de GroofJ, van der SommenF, van der PuttenJ, 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 European Gastroenterol J, 2019; 7(4):538–547; doi: 10.1177/2050640619837443
33.
EbigboA, MendelR, ProbstA, et al.Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut, 2019; 68(7):1143–1145; doi: 10.1136/gutjnl-2018-317573
34.
GhatwaryN, ZolgharniM, YeX. Early esophageal adenocarcinoma detection using deep learning methods. Int J Comput Assist Radiol Surg, 2019; 14(4):611–621; doi: 10.1007/s11548-019-01914-4
35.
LiuG, HuaJ, WuZ, et al.Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network. Ann Transl Med, 2020; 8(7):486; doi: 10.21037/atm.2020.03.24
36.
de GroofAJ, StruyvenbergMR, van der PuttenJ, 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(4):915–929.e4; doi: 10.1016/j.gie.2019.12.048
37.
van der PuttenJ, de GroofJ, StruyvenbergM, et al.Multi-stage domain-specific pretraining for improved detection and localization of Barrett’s neoplasia: A comprehensive clinically validated study. Artif Intell Med, 2020; 107:101914; doi: 10.1016/j.artmed.2020.101914
38.
de SouzaLA, MendelR, StrasserS, et al.Convolutional neural networks for the evaluation of cancer in Barrett’s esophagus: Explainable AI to lighten up the black-box. Comput Biol Med, 2021; 135:104578; doi: 10.1016/j.compbiomed.2021.104578
39.
HashimotoR, RequaJ, DaoT, 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(6):1264–1271.e1; doi: 10.1016/j.gie.2019.12.049
40.
LuiTKL, TsuiVWM, LeungWK. Accuracy of artificial intelligence-assisted detection of upper GI lesions: A systematic review and meta-analysis. Gastrointest Endosc, 2020; 92(4):821–830.e9; doi: 10.1016/j.gie.2020.06.034
41.
TakedaT, AsaokaD, UeyamaH, et al.Development of an artificial intelligence diagnostic system using linked color imaging for Barrett’s esophagus. J Clin Med, 2024; 13(7):1990; doi: 10.3390/jcm13071990
42.
FockensKN, JukemaJB, BoersT, et al.Towards a robust and compact deep learning system for primary detection of early Barrett’s neoplasia: Initial image‐based results of training on a multi‐center retrospectively collected data set. United European Gastroenterol J, 2023; 11(4):324–336; doi: 10.1002/ueg2.12363
43.
TsaiMC, YenHH, TsaiHY, et al.Artificial intelligence system for the detection of Barrett’s esophagus. World J Gastroenterol, 2023; 29(48):6198–6207; doi: 10.3748/wjg.v29.i48.6198
44.
van der LaanJJH, van der PuttenJA, ZhaoX, et al.Optical biopsy of Dysplasia in Barrett’s oesophagus assisted by artificial intelligence. Cancers (Basel), 2023; 15(7):1950; doi: 10.3390/cancers15071950
45.
IwagamiH, IshiharaR, AoyamaK, et al.Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol, 2021; 36(1):131–136; doi: 10.1111/jgh.15136
46.
EbigboA, MendelR, ProbstA, et al.Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus. Gut, 2020; 69(4):615–616; doi: 10.1136/gutjnl-2019-319460
47.
J van derP, StruyvenbergM, de GroofJ, et al.Endoscopy-Driven pretraining for classification of dysplasia in Barrett’s esophagus with endoscopic narrow-band imaging zoom videos. Appl Sci, 2020; 10(10):3407; doi: 10.3390/app10103407
48.
de GroofAJ, StruyvenbergMR, FockensKN, 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(6):1242–1250; doi: 10.1053/j.gastro.2019.11.030
49.
StruyvenbergMR, De GroofAJ, Van Der PuttenJ, et al.A computer-assisted algorithm for narrow-band imaging-based tissue characterization in Barrett’s esophagus. Gastrointest Endosc, 2021; 93(1):89–98; doi: 10.1016/j.gie.2020.05.050
50.
HusseinM, González-Bueno PuyalJ, LinesD, et al.A new artificial intelligence system successfully detects and localises early neoplasia in Barrett’s esophagus by using convolutional neural networks. United European Gastroenterol J, 2022; 10(6):528–537; doi: 10.1002/ueg2.12233
51.
FockensKN, JongMR, JukemaJB, et al.A deep learning system for detection of early Barrett’s neoplasia: A model development and validation study. Lancet Digit Health, 2023; 5(12):e905–16; doi: 10.1016/S2589-7500(23)00199-1
HusseinM, LinesD, González-Bueno PuyalJ, et al.Computer-aided characterization of early cancer in Barrett’s esophagus on i-scan magnification imaging: A multicenter international study. Gastrointest Endosc, 2023; 97(4):646–654; doi: 10.1016/j.gie.2022.11.020
54.
EbigboA, MendelR, ProbstA, et al.Multimodal imaging for detection and segmentation of Barrett’s esophagus-related neoplasia using artificial intelligence. Endoscopy, 2022; 54(10):e587; doi: 10.1055/a-1704-7885
55.
AbdelrahimM, SaikoM, MaedaN, et al.Development and validation of artificial neural networks model for detection of Barrett’s neoplasia: A multicenter pragmatic nonrandomized trial (with video). Gastrointest Endosc, 2023; 97(3):422–434; doi: 10.1016/j.gie.2022.10.031
56.
MeinikheimM, MendelR, PalmC, et al.Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: A tandem randomized and video trial. Endoscopy [Internet], 2024; 56(9):641–649; doi: 10.1055/a-2296-5696
57.
PanW, LiX, WangW, et al.Identification of Barrett’s esophagus in endoscopic images using deep learning. BMC Gastroenterol, 2021; 21(1):479; doi: 10.1186/s12876-021-02055-2
58.
FockensK, JukemaJ, JongM, et al.Video-Based computer aided detection system detects Barrett’s neoplasia with high accuracy during live endoscopy procedures: A multi-center pilot and feasibility study. Gastrointest Endosc, 2023; 97(6, Supplement); doi: 10.1016/j.gie.2023.04.1657
59.
JukemaJB, KustersCHJ, JongMR, et al.Computer-Aided diagnosis improves characterization of Barrett’s neoplasia by general endoscopists. Gastrointest Endosc, 2024; S0016-5107(24)00233-5); doi: 10.1016/j.gie.2024.04.013
60.
KnabeM, WelschL, BlasbergT, et al.Artificial intelligence-assisted staging in Barrett’s carcinoma. Endoscopy, 2022; 54(12):1191–1197; doi: 10.1055/a-1811-9407
61.
EbigboA, MendelR, RückertT, et al.Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of artificial intelligence: A pilot study. Endoscopy, 2021; 53(9):878–883; doi: 10.1055/a-1311-8570
62.
GehrungM, Crispin-OrtuzarM, BermanAG, et al.Triage-driven diagnosis of Barrett’s esophagus for early detection of esophageal adenocarcinoma using deep learning. Nat Med, 2021; 27(5):833–841; doi: 10.1038/s41591-021-01287-9
63.
KambojAK, YarlagaddaMK, PietrowiczM, et al.S402 voice enabled artificial intelligence for detection of pathologic gastroesophageal reflux disease and Barrett’s esophagus. Am J Gastroenterol, 2022; 117(10S):e281–e282; doi: 10.14309/01.ajg.0000858248.86803.d7
64.
FreiNF, KhoshiwalAM, KonteK, et al.Tissue systems pathology test objectively risk stratifies Barrett’s esophagus patients with low-grade dysplasia. Am J Gastroenterol, 2021; 116(4):675–682; doi: 10.14309/ajg.0000000000001037
65.
IyerPG, SachdevaK, LeggettCL, et al.Development of electronic health record-based machine learning models to predict Barrett’s esophagus and esophageal adenocarcinoma risk. Clin Transl Gastroenterol, 2023; 14(10):e00637; doi: 10.14309/ctg.0000000000000637
66.
SaliR, MoradinasabN, GuleriaS, et al.Deep learning for whole-slide tissue histopathology classification: A comparative study in the identification of dysplastic and non-dysplastic Barrett’s esophagus. J Pers Med, 2020; 10(4):141; doi: 10.3390/jpm10040141
67.
GuleriaS, ShahTU, PulidoJV, et al.Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy. Sci Rep, 2021; 11(1):5086; doi: 10.1038/s41598-021-84510-4
68.
CodipillyDC, VogelsangD, AgarwalS, et al.Utilization of a deep learning artificial intelligence model in the histologic diagnosis of dysplasia in Barrett’s esophagus. Gastrointest Endosc, 2021; 93(6):AB288–AB289; doi: 10.1016/j.gie.2021.03.604
69.
Nguyen WenkerT, NatarajanY, CaskeyK, et al.Using natural language processing to automatically identify dysplasia in pathology reports for patients with Barrett’s esophagus. Clin Gastroenterol Hepatol, 2023; 21(5):1198–1204; doi: 10.1016/j.cgh.2022.09.005
70.
TomitaN, AbdollahiB, WeiJ, et al.Attention-Based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides. JAMA Netw Open, 2019; 2(11):e1914645; doi: 10.1001/jamanetworkopen.2019.14645
71.
FaghaniS, CodipillyDC, VogelsangD, et al.Development of a deep learning model for the histologic diagnosis of dysplasia in Barrett’s esophagus. Gastrointest Endosc, 2022; 96(6):918–925.e3; doi: 10.1016/j.gie.2022.06.013
72.
AghaYH, SrinivasanS, HyderJ, et al.WATS3D versus forceps biopsy in screening for Barrett’s esophagus: Experience in community endoscopy centers. Ann Gastroenterol, 2021; 34(2):164–168; doi: 10.20524/aog.2020.0564
73.
RaphaelKL, StewartM, SejpalDV, et al.Adjunctive yield of wide-area transepithelial sampling for dysplasia detection after advanced imaging and random biopsies in Barrett’s Esophagus. Clin Transl Gastroenterol, 2019; 10(12):e00107; doi: 10.14309/ctg.0000000000000107
74.
VennalagantiPR, Naag KanakadandiV, GrossSA, et al.Inter-Observer agreement among pathologists using wide-area transepithelial sampling with computer-assisted analysis in patients with Barrett’s esophagus. Am J Gastroenterol, 2015; 110(9):1257–1260; doi: 10.1038/ajg.2015.116
75.
WuL, ZhangJ, ZhouW, et al.Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut, 2019; 68(12):2161–2169; doi: 10.1136/gutjnl-2018-317366
76.
AliS, BaileyA, AshS, et al.;TGU Investigators. A pilot study on automatic three-dimensional quantification of Barrett’s esophagus for risk stratification and therapy monitoring. Gastroenterology, 2021; 161(3):865–878.e8; doi: 10.1053/j.gastro.2021.05.059
77.
HossainE, AbdelrahimM, TanasescuA, et al.Performance of a novel computer‐aided diagnosis system in the characterization of colorectal polyps, and its role in meeting Preservation and Incorporation of Valuable Endoscopic Innovations standards set by the American society of gastrointestinal endoscopy. DEN Open, 2023; 3(1):e178; doi: 10.1002/deo2.178
78.
HairdyDR. Introducing CADU from Odin Vision. 2022.
79.
PatelA, AroraGS, RoknsharifiM, et al.Artificial intelligence in the detection of Barrett’s Esophagus: A systematic review. Cureus, 2023; 15(10):e47755; doi: 10.7759/cureus.47755
AmannJ, BlasimmeA, VayenaE, et al.;Precise4Q consortium. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med Inform Decis Mak, 2020; 20(1):310; doi: 10.1186/s12911-020-01332-6
82.
SiauK, BerzinTM. Fine‐tuning the artificial intelligence experience in endoscopy. United European Gastroenterol J, 2022; 10(5):449–450; doi: 10.1002/ueg2.12253
83.
BrandM, TroyaJ, KrenzerA, et al.Development and evaluation of a deep learning model to improve the usability of polyp detection systems during interventions. United European Gastroenterol J, 2022; 10(5):477–484; doi: 10.1002/ueg2.12235
84.
DelpishehA, VeisaniY, SayehmiriK, et al.Esophageal carcinoma: Long-term survival in consecutive series of patients through a retrospective cohort study. Gastroenterol Hepatol Bed Bench, 2014; 7(2):101–107.
85.
PopovicD, GlisicT, MilosavljevicT, et al.The importance of artificial intelligence in upper gastrointestinal endoscopy. Diagnostics (Basel), 2023; 13(18):2862; doi: 10.3390/diagnostics13182862
86.
ChenD, WuL, LiY, 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(2):332–339.e3; doi: 10.1016/j.gie.2019.09.016
87.
LiuW, WuY, YuanX, et al.Artificial intelligence-based assessments of colonoscopic withdrawal technique: A new method for measuring and enhancing the quality of fold examination. Endoscopy, 2022; 54(10):972–979; doi: 10.1055/a-1799-8297
88.
ZhouW, YaoL, WuH, et al.Multi-step validation of a deep learning-based system for the quantification of bowel preparation: A prospective, observational study. Lancet Digit Health, 2021; 3(11):e697–706–e706; doi: 10.1016/S2589-7500(21)00109-6
89.
DhaliwalJ, WalshCM. Artificial intelligence in pediatric endoscopy: Current status and future applications. Gastrointest Endosc Clin N Am, 2023; 33(2):291–308; doi: 10.1016/j.giec.2022.12.001
90.
ReidBJ, BlountPL, FengZ, et al.Optimizing endoscopic biopsy detection of early cancers in Barrett’s high-grade dysplasia. Am J Gastroenterol, 2000; 95(11):3089–3096; doi: 10.1016/S0002-9270(00)01975-4
