The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors

Abstract

Gastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor-related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.

Keywords

artificial intelligence / endoscopy / gastrointestinal tumors / multimodal

References

[1] ArnoldM, AbnetCC, NealeRE, et al. Global burden of 5 major types of gastrointestinal cancer. Gastroenterology. 2020;159(1):335-349.e15.

[2] MorsonBC. Precancerous lesions of upper gastrointestinal tract. JAMA. 1962;179:311-315.

[3] CoronE, Robaszkiewicz M, ChatelainD, SvrcekM, Fléjou JF. Advanced precancerous lesions in the lower oesophageal mucosa: high-grade dysplasia and intramucosal carcinoma in Barrett’s oesophagus. Best Pract Res Clin Gastroenterol. 2013;27(2):187-204.

[4] KwanV. Advances in gastrointestinal endoscopy. Intern Med J. 2012;42(2):116-126.

[5] GadoAS, EbeidBA, AxonAT. Quality assurance in gastrointestinal endoscopy: an Egyptian experience. Arab J Gastroenterol. 2016;17(4):153-158.

[6] HowardJ. Artificial intelligence: implications for the future of work. Am J Ind Med. 2019;62(11):917-926.

[7] KourouK, Exarchos TP, ExarchosKP, KaramouzisMV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8-17.

[8] LeCunY, BengioY, HintonG. Deep learning. Nature. 2015;521(7553):436-444.

[9] ShinHC, RothHR, GaoM, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35(5):1285-1298.

[10] El HajjarA, ReyJF. Artificial intelligence in gastrointestinal endoscopy: general overview. Chin Med J. 2020;133(3):326-334.

[11] de GroofJ, van der Sommen F, 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 Eur Gastroenterol J. 2019;7(4):538-547.

[12] RuffleJK, FarmerAD, AzizQ. Artificial intelligence-assisted gastroenterology-promises and pitfalls. Am J Gastroenterol. 2019;114(3):422-428.

[13] SmythEC, Lagergren J, FitzgeraldRC, et al. Oesophageal cancer. Nat Rev Dis Primers. 2017;3:17048.

[14] PennathurA, GibsonMK, JobeBA, Luketich JD. Oesophageal carcinoma. Lancet. 2013;381(9864):400-412.

[15] ArnalMJD, Ferrández Arenas Á, Lanas ArbeloaÁ. Esophageal cancer: risk factors, screening and endoscopic treatment in Western and Eastern countries. World J Gastroenterol. 2015;21(26):7933-7943.

[16] KatzPO, DunbarKB, Schnoll-SussmanFH, GreerKB, Yadlapati R, SpechlerSJ. ACG Clinical Guideline for the Diagnosis and Management of Gastroesophageal Reflux Disease. Am J Gastroenterol. 2022;117(1):27-56.

[17] PaceF, Riegler G, de LeoneA, et al. Is it possible to clinically differentiate erosive from nonerosive reflux disease patients? A study using an artificial neural networks-assisted algorithm. Eur J Gastroenterol Hepatol. 2010;22(10):1163-1168.

[18] HuangCR, ChenYT, ChenWY, Cheng HC, SheuBS. Gastroesophageal reflux disease diagnosis using hierarchical heterogeneous descriptor fusion support vector machine. IEEE Trans Biomed Eng. 2016;63(3):588-599.

[19] WangCC, ChiuYC, ChenWL, Yang TW, TsaiMC, TsengMH. A deep learning model for classification of endoscopic gastroesophageal reflux disease. Int J Environ Res Public Health. 2021;18(5):2428.

[20] KestensC, Offerhaus GJA, van BaalJWPM, SiersemaPD. Patients with Barrett’s esophagus and persistent low-grade dysplasia have an increased risk for high-grade dysplasia and cancer. Clin Gastroenterol Hepatol. 2016;14(7):956-962.

[21] SchlemperRJ. The Vienna classification of gastrointestinal epithelial neoplasia. Gut. 2000;47(2):251-255.

[22] van der SommenF, ZingerS, CurversWL, et al. Computer-aided detection of early neoplastic lesions in Barrett’s esophagus. Endoscopy. 2016;48(7):617-624.

[23] MünzenmayerC, Kage A, WittenbergT, MühldorferS. Computer-assisted diagnosis for precancerous lesions in the esophagus. Methods Inf Med. 2009;48(4):324-330.

[24] EbigboA, MendelR, ProbstA, et al. Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut. 2019;68(7):1143-1145.

[25] GhatwaryN, Zolgharni M, YeX. Early esophageal adenocarcinoma detection using deep learning methods. Inte J Comput Assisted Radiol Surg. 2019;14(4):611-621.

[26] HorieY, YoshioT, AoyamaK, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc. 2019;89(1):25-32.

[27] 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.

[28] 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.

[29] de GroofAJ, Struyvenberg MR, 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.

[30] 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-e916.

[31] VisaggiP, Barberio B, GregoriD, et al. Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases. Aliment Pharmacol Ther. 2022;55(5):528-540.

[32] TrindadeAJ, McKinley MJ, FanC, LeggettCL, KahnA, PleskowDK. Endoscopic surveillance of Barrett’s esophagus using volumetric laser endomicroscopy with artificial intelligence image enhancement. Gastroenterology. 2019;157(2):303-305.

[33] YuanXL, ZengXH, LiuW, et al. Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video). Gastrointest Endosc. 2023;97(4):664-672.

[34] GodaK, Irisawa A. Japan Esophageal Society classification for predicting the invasion depth of superficial esophageal squamous cell carcinoma: should it be modified now? Dig Endosc. 2020;32(1):37-38.

[35] NakagawaK, Ishihara R, AoyamaK, et al. Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists. Gastrointest Endosc. 2019;90(3):407-414.

[36] ShimamotoY, Ishihara R, KatoY, et al. Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence. J Gastroenterol. 2020;55(11):1037-1045.

[37] TokaiY, YoshioT, AoyamaK, et al. Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma. Esophagus. 2020;17(3):250-256.

[38] AminMB, GreeneFL, EdgeSB, et al. The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more “personalized”approach to cancer staging. CA Cancer J Clin. 2017;67(2):93-99.

[39] BrayF, FerlayJ, SoerjomataramI, SiegelRL, TorreLA, JemalA. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424.

[40] HamashimaC, Okamoto M, ShabanaM, OsakiY, Kishimoto T. Sensitivity of endoscopic screening for gastric cancer by the incidence method. Int J Cancer. 2013;133(3):653-659.

[41] JiangK, JiangX, PanJ, et al. Current evidence and future perspective of accuracy of artificial intelligence application for early gastric cancer diagnosis with endoscopy: a systematic and meta-analysis. Front Med. 2021;8:629080.

[42] WuL, HeX, LiuM, et al. Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial. Endoscopy. 2021;53(12):1199-1207.

[43] UeyamaH, KatoY, AkazawaY, 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(2):482-489.

[44] IkenoyamaY, Hirasawa T, IshiokaM, et al. Detecting early gastric cancer: comparison between the diagnostic ability of convolutional neural networks and endoscopists. Dig Endosc. 2021;33(1):141-150.

[45] LuoH, XuG, LiC, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 2019;20(12):1645-1654.

[46] DuH, DongZ, WuL, et al. A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video). Gastric Cancer. 2023;26(2):275-285.

[47] WangL, YangY, YangA, Li T. Lightweight deep learning model incorporating an attention mechanism and feature fusion for automatic classification of gastric lesions in gastroscopic images. Biomed Opt Express. 2023;14(9):4677-4695.

[48] YoonHJ, KimS, KimJH, et al. A Lesion-Based convolutional neural network improves endoscopic detection and depth prediction of early gastric cancer. J Clin Med. 2019;8(9):1310.

[49] KimTY, YiNH, HwangJW, Kim JH, KimGH, KangMS. Morphologic pattern analysis of submucosal deformities identified by endoscopic ultrasonography for predicting the depth of invasion in early gastric cancer. Surg Endosc. 2019;33(7):2169-2180.

[50] ZhuY, WangQC, XuMD, 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(4):806-815.

[51] XieF, ZhangK, LiF, et al. Diagnostic accuracy of convolutional neural network-based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis. Gastrointest Endosc. 2022;95(4):599-609.

[52] TangD, NiM, ZhengC, et al. A deep learning-based model improves diagnosis of early gastric cancer under narrow band imaging endoscopy. Surg Endosc. 2022;36(10):7800-7810.

[53] Chetcuti ZammitS, Sidhu R. Artificial intelligence within the small bowel: are we lagging behind? Curr Opin Gastroenterol. 2022;38(3):307-317.

[54] PanXH, ZhuQ, PanLL, Sun J. Macrophage immunometabolism in inflammatory bowel diseases: from pathogenesis to therapy. Pharmacol Therapeut. 2022;238:108176.

[55] RimolaJ, TorresJ, KumarS, Taylor SA, KucharzikT. Recent advances in clinical practice: advances in cross-sectional imaging in inflammatory bowel disease. Gut. 2022;71(12):2587-2597.

[56] SudhakarP, Wellens J, VerstocktB, FerranteM, SabinoJ, VermeireS. Holistic healthcare in inflammatory bowel disease: time for patient-centric approaches? Gut. 2023;72(1):192-204.

[57] YangY, LiYX, YaoRQ, Du XH, RenC. Artificial intelligence in small intestinal diseases: application and prospects. World J Gastroenterol. 2021;27(25):3734-3747.

[58] Da RioL, Spadaccini M, ParigiTL, et al. Artificial intelligence and inflammatory bowel disease: where are we going? World J Gastroenterol. 2023;29(3):508-520.

[59] Le BerreC, Sandborn WJ, AridhiS, et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology. 2020;158(1):76-94.

[60] ChahalD, ByrneMF. A primer on artificial intelligence and its application to endoscopy. Gastrointest Endosc. 2020;92(4):813-820.e4.

[61] TziortziotisI, Laskaratos FM, CodaS. Role of artificial intelligence in video capsule endoscopy. Diagnostics. 2021;11(7):1192.

[62] DhaliwalJ, ErdmanL, DrysdaleE, et al. Accurate classification of pediatric colonic inflammatory bowel disease subtype using a random forest machine learning classifier. J Pediatr Gastroenterol Nutr. 2021;72(2):262-269.

[63] SmolanderJ, DehmerM, Emmert-StreibF. Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders. FEBS Open Bio. 2019;9(7):1232-1248.

[64] SaitoH, AokiT, AoyamaK, 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(1):144-151.

[65] OtaniK, NakadaA, KuroseY, 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(9):786-791.

[66] FerreiraJPS, de Mascarenhas Saraiva MJQC, AfonsoJPL, et al. Identification of ulcers and erosions by the Novel Pillcam™ Crohn’s Capsule using a convolutional neural network: a multicentre pilot study. J Crohn’s Colitis. 2022;16(1):169-172.

[67] AokiT, YamadaA, KatoY, et al. Automatic detection of various abnormalities in capsule endoscopy videos by a deep learning-based system: a multicenter study. Gastrointest Endosc. 2021;93(1):165-173.

[68] LamashY, Kurugol S, FreimanM, et al. Curved planar reformatting and convolutional neural network-based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn’s disease from MRI. J Magn Reson Imaging. 2019;49(6):1565-1576.

[69] DingZ, ShiH, ZhangH, et al. Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology. 2019;157(4):1044-1054.

[70] RaghavK, Overman MJ. Small bowel adenocarcinomas-existing evidence and evolving paradigms. Nat Rev Clin Oncol. 2013;10(9):534-544.

[71] ChenC, ChenL, LinL, JinD, DuY, LyuJ. Research progress on gut microbiota in patients with gastric cancer, esophageal cancer, and small intestine cancer. Appl Microbiol Biotechnol. 2021;105(11):4415-4425.

[72] SymonsR, DalyD, GandyR, Goldstein D, AghmeshehM. Progress in the treatment of small intestine cancer. Curr Treat Options Oncol. 2023;24(4):241-261.

[73] LiuG, YanG, KuangS, 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.

[74] VieiraPM, Freitas NR, ValenteJ, VazIF, Rolanda C, LimaCS. Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning. Med Phys. 2020;47(1):52-63.

[75] LiBP, MengMQH. Comparison of several texture features for tumor detection in CE images. J Med Syst. 2012;36(4):2463-2469.

[76] KjellmanM, KniggeU, WelinS, et al. A plasma protein biomarker strategy for detection of small intestinal neuroendocrine tumors. Neuroendocrinology. 2021;111(9):840-849.

[77] YanJ, ZhaoX, HanS, WangT, MiaoF. Evaluation of clinical plus imaging features and multidetector computed tomography texture analysis in preoperative risk grade prediction of small bowel gastrointestinal stromal tumors. J Comput Assist Tomogr. 2018;42(5):714-720.

[78] SumiokaA, TsuboiA, OkaS, et al. Disease surveillance evaluation of primary small-bowel follicular lymphoma using capsule endoscopy images based on a deep convolutional neural network (with video). Gastrointest Endosc. 2023;98(6):968-976.

[79] ZhouJX, YangZ, XiDH, et al. Enhanced segmentation of gastrointestinal polyps from capsule endoscopy images with artifacts using ensemble learning. World J Gastroenterol. 2022;28(41):5931-5943.

[80] ShaukatA, KahiCJ, BurkeCA, Rabeneck L, SauerBG, RexDK. ACG clinical guidelines: colorectal cancer screening 2021. Am J Gastroenterol. 2021;116(3):458-479.

[81] CorleyDA, JensenCD, MarksAR, et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014;370(14):1298-1306.

[82] OrtolaniJB, Tershak DR, FerraraJJ, PagetCJ. The goalposts have moved: can surgery residents meet updated quality benchmarks for adenoma detection rate in colonoscopy? Am Surg. 2016;82(9):835-838.

[83] UrbanG, Tripathi P, AlkayaliT, et al. Deep learning localizes and identifies polyps in real time with 96%accuracy in screening colonoscopy. Gastroenterology. 2018;155(4):1069-1078.e8.

[84] BaruaI, Vinsard DG, JodalHC, et al. Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis. Endoscopy. 2021;53(3):277-284.

[85] HuangD, ShenJ, HongJ, et al. Effect of artificial intelligence-aided colonoscopy for adenoma and polyp detection: a meta-analysis of randomized clinical trials. Int J Colorectal Dis. 2022;37(3):495-506.

[86] DeliwalaSS, HamidK, BarbarawiM, et al. Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis. Int J Colorectal Dis. 2021;36(11):2291-2303.

[87] KarsentiD, Tharsis G, PerrotB, et al. Effect of real-time computer-aided detection of colorectal adenoma in routine colonoscopy (COLO-GENIUS): a single-centre randomised controlled trial. Lancet Gastroenterol Hepatol. 2023;8(8):726-734.

[88] WeiMT, Shankar U, ParvinR, et al. Evaluation of computer-aided detection during colonoscopy in the community (AI-SEE): a multicenter randomized clinical trial. Am J Gastroenterol. 2023;118(10):1841-1847.

[89] XuH, TangRSY, LamTYT, et al. Artificial intelligence-assisted colonoscopy for colorectal cancer screening: a multicenter randomized controlled trial. Clin Gastroenterol Hepatol. 2023;21(2):337-346.

[90] KobayashiN, SaitoY, SanoY, et al. Determining the treatment strategy for colorectal neoplastic lesions: endoscopic assessment or the non-lifting sign for diagnosing invasion depth? Endoscopy. 2007;39(8):701-705.

[91] Pimentel-NunesP, Dinis-Ribeiro M, PonchonT, et al. Endoscopic submucosal dissection: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Endoscopy. 2015;47(9):829-854.

[92] LuoX, WangJ, HanZ, et al. Artificial intelligence-enhanced white-light colonoscopy with attention guidance predicts colorectal cancer invasion depth. Gastrointest Endosc. 2021;94(3):627-638.

[93] YaoL, LuZ, YangG, et al. Development and validation of an artificial intelligence-based system for predicting colorectal cancer invasion depth using multi-modal data. Dig Endosc. 2023;35(5):625-635.

[94] KandagatlaP, Maguire LH, HardimanKM. Biology of nodal spread in colon cancer: insights from molecular and genetic studies. Eur Surg Res. 2018;59(5-6):361-370.

[95] IchimasaK, KudoS, MiyachiH, Kouyama Y, MisawaM, MoriY. Risk stratification of T1 colorectal cancer metastasis to lymph nodes: current status and perspective. Gut Liver. 2021;15(6):818-826.

[96] PellinoG, WarrenO, MillsS, Rasheed S, TekkisPP, KontovounisiosC. Comparison of Western and Asian guidelines concerning the management of colon cancer. Dis Colon Rectum. 2018;61(2):250-259.

[97] KudoS, Ichimasa K, VillardB, et al. Artificial intelligence system to determine risk of T1 colorectal cancer metastasis to lymph node. Gastroenterology. 2021;160(4):1075-1084.

[98] IchimasaK, Nakahara K, KudoS-E, et al. Novel “resect and analysis”approach for T2 colorectal cancer with use of artificial intelligence. Gastrointest Endosc. 2022;96(4):665-672.

[99] AvellaP, Cappuccio M, CappuccioT, et al. Artificial intelligence to early predict liver metastases in patients with colorectal cancer: current status and future prospectives. Life. 2023;13(10):2027.

[100] ZhangK, WangH, ChengY, et al. Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images. Sci Rep. 2024;14(1):7847.

[101] Dos SantosCEO, Malaman D, Arciniegas SanmartinID, LeãoABS, LeãoGS, Pereira-Lima JC. Performance of artificial intelligence in the characterization of colorectal lesions. Saudi J Gastroenterol. 2023;29(4):219-224.

[102] KaderR, Cid-Mejias A, BrandaoP, et al. Polyp characterization using deep learning and a publicly accessible polyp video database. Dig Endosc. 2023;35(5):645-655.

[103] 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 Eur Gastroenterol J. 2023;11(4):324-336.

[104] 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.

[105] MazumdarS, SinhaS, JhaS, JagtapB. Computer-aided automated diminutive colonic polyp detection in colonoscopy by using deep machine learning system;first indigenous algorithm developed in India. Indian J Gastroenterol. 2023;42(2):226-232.

[106] ChinoA, IdeD, AbeS, et al. Performance evaluation of a computer-aided polyp detection system with artificial intelligence for colonoscopy. Dig Endosc. 2024;36(2):185-194.

[107] KnabeM, WelschL, BlasbergT, et al. Artificial intelligence-assisted staging in Barrett’s carcinoma. Endoscopy. 2022;54(12):1191-1197.

[108] TangD, WangL, JiangJ, et al. A novel deep learning system for diagnosing early esophageal squamous cell carcinoma: a multicenter diagnostic study. Clin Transl Gastroenterol. 2021;12(8):e00393.

[109] YuanXL, LiuW, LiuY, et al. Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study. Surg Endosc. 2022;36(11):8651-8662.

[110] SakamotoT, Nakashima H, NakamuraK, NagahamaR, SaitoY. Performance of Computer-Aided detection and diagnosis of colorectal polyps compares to that of experienced endoscopists. Dig Dis Sci. 2022;67(8):3976-3983.

[111] MinodaY, IharaE, FujimoriN, et al. Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors. Sci Rep. 2022;12(1):16640.

[112] NiikuraR, AokiT, ShichijoS, et al. Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy. Endoscopy. 2022;54(8):780-784.

[113] HeX, WuL, DongZ, et al. Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos). Gastrointest Endosc. 2022;95(4):671-678.

[114] SaraivaMM, Ferreira JPS, CardosoH, et al. Artificial intelligence and colon capsule endoscopy: development of an automated diagnostic system of protruding lesions in colon capsule endoscopy. Tech Coloproctol. 2021;25(11):1243-1248.

[115] 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.

[116] ChoiSJ, KimES, ChoiK. Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms. Sci Rep. 2021;11(1):5311.

[117] KudoS-E, MisawaM, MoriY, et al. Artificial intelligence-assisted system improves endoscopic identification of colorectal neoplasms. Clin Gastroenterol Hepatol. 2020;18(8):1874-1881.

[118] TangD, WangL, LingT, 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.

[119] HoriuchiY, AoyamaK, TokaiY, et al. Convolutional neural network for differentiating gastric cancer from gastritis using magnified endoscopy with narrow band imaging. Dig Dis Sci. 2020;65(5):1355-1363.

[120] SongEM, ParkB, HaCA, et al. Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model. Sci Rep. 2020;10(1):30.

[121] ZhouD, TianF, TianX, et al. Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer. Nat Commun. 2020;11(1):2961.

[122] KumagaiY, TakuboK, KawadaK, et al. Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus. Esophagus. 2019;16(2):180-187.

[123] YamadaM, SaitoY, ImaokaH, et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep. 2019;9(1):14465.

[124] ChenPJ, LinMC, LaiMJ, Lin JC, LuHHS, TsengVS. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology. 2018;154(3):568-575.

[125] TakedaK, KudoS, MoriY, et al. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy. Endoscopy. 2017;49(8):798-802.

[126] QiX, SivakMV, IsenbergG, Willis JE, RollinsAM. Computer-aided diagnosis of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography. J Biomed Opt. 2006;11(4):044010.

[127] BarbosaDC, RouparDB, RamosJC, Tavares AC, LimaCS. Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images. Biomed Eng Online. 2012;11:3.

[128] AhmadOF, González-Bueno Puyal J, BrandaoP, et al. Performance of artificial intelligence for detection of subtle and advanced colorectal neoplasia. Dig Endosc. 2022;34(4):862-869.

[129] ArifAA, JiangSX, ByrneMF. Artificial intelligence in endoscopy: overview, applications, and future directions. Saudi J Gastroenterol. 2023;29(5):269-277.

[130] MitsalaA, Tsalikidis C, PitiakoudisM, SimopoulosC, Tsaroucha AK. Artificial intelligence in colorectal cancer screening, diagnosis and treatment. A new era. Curr Oncol. 2021;28(3):1581-1607.

[131] ChinSE, WanFT, LadladJ, et al. One-year review of real-time artificial intelligence (AI)-aided endoscopy performance. Surg Endosc. 2023;37(8):6402-6407.

[132] LiJ, LuJ, YanJ, TanY, LiuD. Artificial intelligence can increase the detection rate of colorectal polyps and adenomas: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol. 2021;33(8):1041-1048.

[133] YaoL, LiX, WuZ, et al. Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study. Gastrointest Endosc. 2024;99(1):91-99.

[134] YamaguchiD, Shimoda R, MiyaharaK, et al. Impact of an artificial intelligence-aided endoscopic diagnosis system on improving endoscopy quality for trainees in colonoscopy: prospective, randomized, multicenter study. Dig Endosc. 2024;36(1):40-48.

[135] ZippeliusC, Alqahtani SA, SchedelJ, et al. Diagnostic accuracy of a novel artificial intelligence system for adenoma detection in daily practice: a prospective nonrandomized comparative study. Endoscopy. 2022;54(5):465-472.

[136] KambaS, TamaiN, SaitohI, et al. Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial. J Gastroenterol. 2021;56(8):746-757.

[137] NakashimaH, Kitazawa N, FukuyamaC, et al. Clinical evaluation of Computer-Aided colorectal neoplasia detection using a novel endoscopic artificial intelligence: a single-center randomized controlled trial. Digestion. 2023;104(3):193-201.

[138] BrownJRG, Mansour NM, WangP, 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(7):1499-1507.

[139] YaoL, ZhangL, LiuJ, 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. 2022;54(8):757-768.

[140] SchölerJ, Alavanja M, de LangeT, YamamotoS, Hedenström P, VarkeyJ. Impact of AI-aided colonoscopy in clinical practice: a prospective randomised controlled trial. BMJ Open Gastroenterol. 2024;11(1):e001247.

[141] DesaiM, AuskK, BrannanD, et al. Use of a novel artificial intelligence system leads to the detection of significantly higher number of adenomas during screening and surveillance colonoscopy: results from a large, prospective, US multicenter, randomized clinical trial. Am J Gastroenterol. 2024;119(7):1383-1391.

[142] Gimeno-GarcíaAZ, Hernández NegrinD, Hernández A, et al. Usefulness of a novel computer-aided detection system for colorectal neoplasia: a randomized controlled trial. Gastrointest Endosc. 2023;97(3):528-536.

[143] AhmadA, WilsonA, HaycockA, et al. Evaluation of a real-time computer-aided polyp detection system during screening colonoscopy: AI-DETECT study. Endoscopy. 2023;55(4):313-319.

[144] WallaceMB, SharmaP, BhandariP, et al. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology. 2022;163(1):295-304.

[145] WangP, LiuP, Glissen BrownJR, 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(4):1252-1261.

[146] KohFH, LadladJ, FooFJ, et al. Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore. Surg Endosc. 2023;37(1):165-171.

[147] RepiciA, Spadaccini M, AntonelliG, et al. Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut. 2022;71(4):757-765.

[148] QuanSY, WeiMT, LeeJ, et al. Clinical evaluation of a real-time artificial intelligence-based polyp detection system: a US multi-center pilot study. Sci Rep. 2022;12(1):6598.

[149] IshiyamaM, KudoS, MisawaM, et al. Impact of the clinical use of artificial intelligence-assisted neoplasia detection for colonoscopy: a large-scale prospective, propensity score-matched study (with video). Gastrointest Endosc. 2022;95(1):155-163.

[150] WuL, ShangR, SharmaP, et al. Effect of a deep learning-based system on the miss rate of gastric neoplasms during upper gastrointestinal endoscopy: a single-centre, tandem, randomised controlled trial. Lancet Gastroenterol Hepatol. 2021;6(9):700-708.

[151] LuoY, ZhangY, LiuM, et al. Artificial Intelligence-Assisted colonoscopy for detection of colon polyps: a prospective, randomized cohort study. J Gastrointest Surg. 2021;25(8):2011-2018.

[152] XuJ, KuaiY, ChenQ, Wang X, ZhaoY, SunB. Spatio-Temporal feature transformation based polyp recognition for automatic detection: higher accuracy than novice endoscopists in colorectal polyp detection and diagnosis. Dig Dis Sci. 2024;69(3):911-921.

[153] WuL, WangJ, HeX, et al. Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos). Gastrointest Endosc. 2022;95(1):92-104.

[154] RepiciA, Badalamenti M, MaselliR, et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology. 2020;159(2):512-520.e7.

[155] JinEH, LeeD, BaeJH, et al. Improved accuracy in optical diagnosis of colorectal polyps using convolutional neural networks with visual explanations. Gastroenterology. 2020;158(8):2169-2179.

[156] KwakMS, ChaJM, JeonJW, Yoon JY, ParkJW. Artificial intelligence-based measurement outperforms current methods for colorectal polyp size measurement. Dig Endosc. 2022;34(6):1188-1195.

[157] HannA, TroyaJ, FittingD. Current status and limitations of artificial intelligence in colonoscopy. United Eur Gastroenterol J. 2021;9(5):527-533.

[158] SinonquelP, Eelbode T, BossuytP, MaesF, Bisschops R. Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy. Dig Endosc. 2021;33(2):242-253.

[159] 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.

[160] ZhuC, HuaY, ZhangM, et al. A multimodal multipath artificial intelligence system for diagnosing gastric protruded lesions on endoscopy and endoscopic ultrasonography images. Clin Transl Gastroenterol. 2023;14(10):e00551.

[161] LuZ, XuY, YaoL, et al. Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video). Gastrointest Endosc. 2022;95(6):1186-1194.e3.

[162] ShiT, JiangH, ZhengB. A stacked generalization u-shape network based on zoom strategy and its application in biomedical image segmentation. Comput Methods Programs Biomed. 2020;197:105678.

[163] TanwarS, Vijayalakshmi S, SabharwalM, KaurM, AlZubiAA, LeeHN. Detection and classification of colorectal polyp using deep learning. BioMed Res Int. 2022;2022:2805607.

[164] HassanC, Spadaccini M, MoriY, et al. Real-time computer-aided detection of colorectal neoplasia during colonoscopy: a systematic review and meta-analysis. Ann Intern Med. 2023;176(9):1209-1220.

[165] MannathJ, Ragunath K. Role of endoscopy in early oesophageal cancer. Nat Rev Gastroenterol Hepatol. 2016;13(12):720-730.

[166] BuchnerAM, ShahidMW, HeckmanMG, et al. High-Definition colonoscopy detects colorectal polyps at a higher rate than standard white-light colonoscopy. Clin Gastroenterol Hepatol. 2010;8(4):364-370.

[167] SubramanianV, Mannath J, HawkeyC, RagunathK. High definition colonoscopy vs. standard video endoscopy for the detection of colonic polyps: a meta-analysis. Endoscopy. 2011;43(6):499-505.

[168] SamiSS, Subramanian V, ButtWM, et al. High definition versus standard definition white light endoscopy for detecting dysplasia in patients with Barrett’s esophagus: HD endoscopy in Barrett’s surveillance. Dis Esophagus. 2015;28(8):742-749.

[169] VinczeA. Endoscopic diagnosis and treatment in gastric cancer: current evidence and new perspectives. Front Surg. 2023;10:1122454.

[170] SubramanianV, Ramappa V, TelakisE, et al. Comparison of high definition with standard white light endoscopy for detection of dysplastic lesions during surveillance colonoscopy in patients with colonic inflammatory bowel disease. Inflamm Bowel Dis. 2013;19(2):350-355.

[171] KiesslichR, Fritsch J, HoltmannM, et al. Methylene blue-aided chromoendoscopy for the detection of intraepithelial neoplasia and colon cancer in ulcerative colitis. Gastroenterology. 2003;124(4):880-888.

[172] RutterMD, Saunders BP, SchofieldG, ForbesA, PriceAB, TalbotIC. Pancolonic indigo carmine dye spraying for the detection of dysplasia in ulcerative colitis. Gut. 2004;53(2):256-260.

[173] FortunPJ, Anagnostopoulos GK, KayeP, et al. Acetic acid-enhanced magnification endoscopy in the diagnosis of specialized intestinal metaplasia, dysplasia and early cancer in Barrett’s oesophagus. Aliment Pharmacol Ther. 2006;23(6):735-742.

[174] BhandariP, Kandaswamy P, CowlishawD, Longcroft-WheatonG. Acetic acid-enhanced chromoendoscopy is more cost-effective than protocol-guided biopsies in a high-risk Barrett’s population: cost effectiveness of acetic acid in Barrett’s. Dis Esophagus. 2012;25(5):386-392.

[175] KodashimaS. Novel image-enhanced endoscopy with i-scan technology. World J Gastroenterol. 2010;16(9):1043-1049.

[176] YangYJ. Current status of image-enhanced endoscopy in inflammatory bowel disease. Clin Endosc. 2023;56(5):563-577.

[177] YaoK. Use of magnifying endoscopy with narrow-band imaging can change the clinical practice of screening endoscopy for early upper gastrointestinal neoplasia. Dig Endosc. 2022;34(5):1010-1011.

[178] IdeE, MalufF, ChavesDM, Matuguma SE, SakaiP. Narrow-band imaging without magnification for detecting early esophageal squamous cell carcinoma. World J Gastroenterol. 2011;17(39):4408-4413.

[179] KuboM, OnoS, DohiO, et al. Surveillance esophagogastroduodenoscopy using linked color imaging and narrow-band imaging: a multicenter randomized controlled trial. J Gastroenterol Hepatol. 2024;39(6):1065-1072.

[180] HoriuchiY, Fujisaki J, YamamotoN, et al. Accuracy of diagnostic demarcation of undifferentiated-type early gastric cancer for magnifying endoscopy with narrow-band imaging: surgical cases. Surg Endosc. 2017;31(4):1906-1913.

[181] KurumiH, NonakaK, IkebuchiY, et al. Fundamentals, diagnostic capabilities, and perspective of narrow band imaging for early gastric cancer. J Clin Med. 2021;10(13):2918.

[182] DesaiM, Boregowda U, SrinivasanS, et al. Narrow band imaging for detection of gastric intestinal metaplasia and dysplasia: a systematic review and meta-analysis. J Gastroenterol Hepatol. 2021;36(8):2038-2046.

[183] KudoT, Matsumoto T, EsakiM, YaoT, IidaM. Mucosal vascular pattern in ulcerative colitis: observations using narrow band imaging colonoscopy with special reference to histologic inflammation. Int J Colorectal Dis. 2009;24(5):495-501.

[184] LeifeldL, RoglerG, StallmachA, et al. White-light or narrow-band imaging colonoscopy in surveillance of ulcerative colitis: a prospective multicenter study. Clin Gastroenterol Hepatol. 2015;13(10):1776-1781.

[185] VaculováJ, Kroupa R, KalaZ, et al. The use of confocal laser endomicroscopy in diagnosing Barrett’s esophagus and esophageal adenocarcinoma. Diagnostics. 2022;12(7):1616.

[186] ChauhanSS, Abu Dayyeh BK, BhatYM, et al. Confocal laser endomicroscopy. Gastrointest Endosc. 2014;80(6):928-938.

[187] KiesslichR, Gossner L, GoetzM, et al. In vivo histology of Barrett’s esophagus and associated neoplasia by confocal laser endomicroscopy. Clin Gastroenterol Hepatol. 2006;4(8):979-987.

[188] PechO, Rabenstein T, MannerH, et al. Confocal laser endomicroscopy for in vivo diagnosis of early squamous cell carcinoma in the esophagus. Clin Gastroenterol Hepatol. 2008;6(1):89-94.

[189] PilonisND, Januszewicz W, diPietroM. Confocal laser endomicroscopy in gastro-intestinal endoscopy: technical aspects and clinical applications. Transl Gastroent Hep. 2022;7:7.

[190] RasmussenDN, Karstensen JG, RiisLB, BrynskovJ, Vilmann P. Confocal laser endomicroscopy in inflammatory bowel disease-a systematic review. J Crohn’s Colitis. 2015;9(12):1152-1159.

[191] GómezV, Buchner A, DekkerE, et al. Interobserver agreement and accuracy among international experts with probe-based confocal laser endomicroscopy in predicting colorectal neoplasia. Endoscopy. 2010;42(4):286-291.

[192] XinYP, ZhangQ, LiuXY, Li BQ, MaoT, LiXY. Application of artificial intelligence in endoscopic gastrointestinal tumors. Front Oncol. 2023;13:1239788.

RIGHTS & PERMISSIONS

2024 2024 The Author(s). MedComm – Oncology published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).