{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T22:08:58Z","timestamp":1791756538384,"version":"4.3.4"},"reference-count":71,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T00:00:00Z","timestamp":1693872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer\u2019s ability to achieve human cognitive performance, which is accomplished through enabling computer \u201clearning\u201d. This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett\u2019s esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.<\/jats:p>","DOI":"10.3390\/diagnostics13182862","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T09:59:31Z","timestamp":1693907971000},"page":"2862","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5912-0360","authenticated-orcid":false,"given":"Dusan","family":"Popovic","sequence":"first","affiliation":[{"name":"Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia"},{"name":"Department of Gastroenterology, Clinical Hospital Center \u201cDr Dragisa Misovic-Dedinje\u201d, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6242-7275","authenticated-orcid":false,"given":"Tijana","family":"Glisic","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia"},{"name":"Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4396-7532","authenticated-orcid":false,"given":"Tomica","family":"Milosavljevic","sequence":"additional","affiliation":[{"name":"General Hospital \u201cEuromedic\u201d, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natasa","family":"Panic","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Clinical Hospital Center \u201cDr Dragisa Misovic-Dedinje\u201d, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7053-9034","authenticated-orcid":false,"given":"Marija","family":"Marjanovic-Haljilji","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Clinical Hospital Center \u201cDr Dragisa Misovic-Dedinje\u201d, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9386-4056","authenticated-orcid":false,"given":"Dragana","family":"Mijac","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia"},{"name":"Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4836-4439","authenticated-orcid":false,"given":"Milica","family":"Stojkovic Lalosevic","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia"},{"name":"Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jelena","family":"Nestorov","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia"},{"name":"Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1944-3883","authenticated-orcid":false,"given":"Sanja","family":"Dragasevic","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia"},{"name":"Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Predrag","family":"Savic","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia"},{"name":"Clinic for Surgery, Clinical Hospital Center \u201cDr Dragisa Misovic-Dedinje\u201d, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Branka","family":"Filipovic","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia"},{"name":"Department of Gastroenterology, Clinical Hospital Center \u201cDr Dragisa Misovic-Dedinje\u201d, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.gie.2020.06.040","article-title":"History of artificial intelligence in medicine","volume":"92","author":"Kaul","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1159\/000518232","article-title":"Artificial Intelligence in Upper Gastrointestinal Endoscopy","volume":"40","author":"Tokat","year":"2022","journal-title":"Dig. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1007\/s10620-021-07086-z","article-title":"Artificial Intelligence in Endoscopy","volume":"67","author":"Okagawa","year":"2022","journal-title":"Dig. Dis. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"E1616","DOI":"10.1055\/a-1010-5705","article-title":"A technical review of artificial intelligence as applied to gastrointestinal endoscopy: Clarifying the terminology","volume":"7","author":"Ebigbo","year":"2019","journal-title":"Endosc. Int. Open"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1111\/den.13317","article-title":"Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective","volume":"31","author":"Mori","year":"2019","journal-title":"Dig. Endosc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26317745211049964","DOI":"10.1177\/26317745211049964","article-title":"Artificial intelligence in Barrett\u2019s Esophagus","volume":"14","author":"Hamade","year":"2021","journal-title":"Ther. Adv. Gastrointest. Endosc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Renna, F., Martins, M., Neto, A., Cunha, A., Lib\u00e2nio, D., Dinis-Ribeiro, M., and Coimbra, M. (2022). Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice. Diagnostics, 12.","DOI":"10.3390\/diagnostics12051278"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1055\/a-1675-4136","article-title":"Prevalence and risk factors of upper gastrointestinal cancers missed during endoscopy: A nationwide registry-based study","volume":"54","author":"Januszewicz","year":"2022","journal-title":"Endoscopy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1016\/j.gie.2020.03.064","article-title":"Deep learning algorithm detection of Barrett\u2019s neoplasia with high accuracy during live endoscopic procedures: A pilot study (with video)","volume":"91","author":"Struyvenberg","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1136\/gutjnl-2019-319460","article-title":"Real-time use of artificial intelligence in the evaluation of cancer in Barrett\u2019s oesophagus","volume":"69","author":"Ebigbo","year":"2020","journal-title":"Gut"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/s41572-019-0086-z","article-title":"Barrett oesophagus","volume":"5","author":"Peters","year":"2019","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1053\/j.gastro.2019.11.030","article-title":"Deep-Learning System Detects Neoplasia in Patients with Barrett\u2019s Esophagus with Higher Accuracy Than Endoscopists in a Multistep Training and Validation Study with Benchmarking","volume":"158","author":"Struyvenberg","year":"2020","journal-title":"Gastroenterology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/S0140-6736(12)60643-6","article-title":"Oesophageal carcinoma","volume":"381","author":"Pennathur","year":"2013","journal-title":"Lancet"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1055\/s-0042-122140","article-title":"Endoscopic management of Barrett\u2019s esophagus: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement","volume":"49","author":"Weusten","year":"2017","journal-title":"Endoscopy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.gie.2013.04.167","article-title":"Endoscopic mucosal tissue sampling","volume":"78","author":"Sharaf","year":"2013","journal-title":"Gastrointest. Endosc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1053\/j.gastro.2006.08.032","article-title":"The Development and Validation of an Endoscopic Grading System for Barrett\u2019s Esophagus: The Prague C & M Criteria","volume":"131","author":"Sharma","year":"2006","journal-title":"Gastroenterology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1111\/den.14342","article-title":"Global variations in diagnostic guidelines for Barrett\u2019s esophagus","volume":"34","author":"Kusano","year":"2022","journal-title":"Dig. Endosc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1159\/000495849","article-title":"Accuracy and Pitfalls in the Assessment of Early Gastrointestinal Lesions","volume":"37","author":"Milosavljevic","year":"2019","journal-title":"Dig. Dis."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e72","DOI":"10.1002\/deo2.72","article-title":"Implementation of artificial intelligence in upper gastrointestinal endoscopy","volume":"2","author":"Nagao","year":"2022","journal-title":"DEN Open"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17048","DOI":"10.1038\/nrdp.2017.48","article-title":"Oesophageal cancer","volume":"3","author":"Smyth","year":"2017","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1055\/s-0042-105284","article-title":"Computer-aided detection of early neoplastic lesions in Barrett\u2019s esophagus","volume":"48","author":"Zinger","year":"2016","journal-title":"Endoscopy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1016\/j.gie.2020.06.034","article-title":"Accuracy of artificial intelligence\u2013assisted detection of upper GI lesions: A systematic review and meta-analysis","volume":"92","author":"Lui","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1002\/ueg2.12363","article-title":"Towards a robust and compact deep learning system for primary detection of early Barrett\u2019s neoplasia: Initial image-based results of training on a multi-center retrospectively collected data set","volume":"11","author":"Fockens","year":"2023","journal-title":"United Eur. Gastroenterol. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.gie.2022.10.031","article-title":"Development and validation of artificial neural networks model for detection of Barrett\u2019s neoplasia: A multicenter pragmatic nonrandomized trial (with video)","volume":"97","author":"Abdelrahim","year":"2023","journal-title":"Gastrointest. Endosc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.gie.2020.05.050","article-title":"A computer-assisted algorithm for narrow-band imaging-based tissue characterization in Barrett\u2019s esophagus","volume":"93","author":"Struyvenberg","year":"2021","journal-title":"Gastrointest. Endosc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.gie.2017.03.011","article-title":"Computer-aided detection of early Barrett\u2019s neoplasia using volumetric laser endomicroscopy","volume":"86","author":"Swager","year":"2017","journal-title":"Gastrointest. Endosc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1111\/apt.16778","article-title":"Systematic review with meta-analysis: Artificial intelligence in the diagnosis of oesophageal diseases","volume":"55","author":"Visaggi","year":"2022","journal-title":"Aliment. Pharmacol. Ther."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1016\/j.gie.2019.12.049","article-title":"Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett\u2019s esophagus (with video)","volume":"91","author":"Hashimoto","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1053\/j.gastro.2017.08.023","article-title":"Epidemiology of Esophageal Squamous Cell Carcinoma","volume":"154","author":"Abnet","year":"2018","journal-title":"Gastroenterology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1159\/000439473","article-title":"Diagnostics and Early Diagnosis of Esophageal Cancer","volume":"31","author":"Meves","year":"2015","journal-title":"Visc. Med."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Morita, F.H.A., Bernardo, W.M., Ide, E., Rocha, R.S.P., Aquino, J.C.M., Minata, M.K., Yamazaki, K., Marques, S.B., Sakai, P., and de Moura, E.G.H. (2017). Narrow band imaging versus lugol chromoendoscopy to diagnose squamous cell carcinoma of the esophagus: A systematic review and meta-analysis. BMC Cancer, 17.","DOI":"10.1186\/s12885-016-3011-9"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.gie.2018.04.2352","article-title":"Screening for esophageal squamous cell carcinoma: Recent advances","volume":"88","author":"Codipilly","year":"2018","journal-title":"Gastrointest. Endosc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1016\/j.dld.2007.03.004","article-title":"Ex vivo pilot study using computed analysis of endo-cytoscopic images to differentiate normal and malignant squamous cell epithelia in the oesophagus","volume":"39","author":"Kodashima","year":"2007","journal-title":"Dig. Liver Dis."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.gie.2018.07.037","article-title":"Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks","volume":"89","author":"Horie","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s12672-023-00694-3","article-title":"Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system","volume":"14","author":"Feng","year":"2023","journal-title":"Discov. Oncol."},{"key":"ref_36","first-page":"395","article-title":"Establishment and clinical validation of an artificial intelligence YOLOv51 model for the detection of precancerous lesions and superficial esophageal cancer in endoscopic procedure","volume":"44","author":"Wang","year":"2022","journal-title":"Chin. J. Oncol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s00535-020-01716-5","article-title":"Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence","volume":"55","author":"Shimamoto","year":"2020","journal-title":"J. Gastroenterol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1111\/jgh.15689","article-title":"Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: A multicenter study","volume":"37","author":"Yuan","year":"2022","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.gie.2019.09.034","article-title":"Endoscopic detection and differentiation of esophageal lesions using a deep neural network","volume":"91","author":"Ohmori","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.gie.2019.08.018","article-title":"Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos)","volume":"91","author":"Guo","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.gie.2019.04.245","article-title":"Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists","volume":"90","author":"Nakagawa","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.17849\/insm-48-1-1-19.1","article-title":"20-Year Comparative Survival and Mortality of Cancer of the Stomach by Age, Sex, Race, Stage, Grade, Cohort Entry Time-Period, Disease Duration & Selected ICD-O-3 Oncologic Phenotypes: A Systematic Review of 157,258 Cases for Diagnosis Years 1973\u20132014: (SEER*Stat 8.3.4)","volume":"48","author":"Milano","year":"2019","journal-title":"J. Insur. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1007\/s00432-020-03304-9","article-title":"Artificial intelligence in gastric cancer: A systematic review","volume":"146","author":"Jin","year":"2020","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1080\/17474124.2021.1845140","article-title":"A comprehensive update on early gastric cancer: Defining terms, etiology, and alarming risk factors","volume":"15","author":"Yang","year":"2021","journal-title":"Expert Rev. Gastroenterol. Hepatol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/S0140-6736(09)60617-6","article-title":"Gastric cancer","volume":"374","author":"Hartgrink","year":"2009","journal-title":"Lancet"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5126","DOI":"10.3748\/wjg.v27.i31.5126","article-title":"Endoscopic diagnosis and treatment of gastric dysplasia and early cancer: Current evidence and what the future may hold","volume":"27","author":"Young","year":"2021","journal-title":"World J. Gastroenterol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1111\/den.13684","article-title":"Guidelines for endoscopic diagnosis of early gastric cancer","volume":"32","author":"Yao","year":"2020","journal-title":"Dig. Endosc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1136\/flgastro-2018-101089","article-title":"Recent advances in the detection and management of early gastric cancer and its precursors","volume":"12","author":"Waddingham","year":"2020","journal-title":"Front. Gastroenterol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1111\/jgh.12149","article-title":"Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement","volume":"28","author":"Miyaki","year":"2013","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/S1470-2045(19)30637-0","article-title":"Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: A multicentre, case-control, diagnostic study","volume":"20","author":"Luo","year":"2019","journal-title":"Lancet Oncol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1007\/s00464-011-2036-z","article-title":"Medical image analysis: Computer-aided diagnosis of gastric cancer invasion on endoscopic images","volume":"26","author":"Kubota","year":"2021","journal-title":"Surg. Endosc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1055\/a-1660-6500","article-title":"Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy","volume":"54","author":"Niikura","year":"2022","journal-title":"Endoscopy"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1590\/s0004-2803.20220300075","article-title":"Efficacy analysis of endoscopic submucosal dissection for the early gastric cancer and precancerous lesions","volume":"59","author":"Costa","year":"2022","journal-title":"Arq. Gastroenterol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5408","DOI":"10.3748\/wjg.v26.i36.5408","article-title":"Artificial intelligence in gastric cancer: Application and future perspectives","volume":"26","author":"Niu","year":"2020","journal-title":"World J. Gastroenterol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"103146","DOI":"10.1016\/j.ebiom.2020.103146","article-title":"Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study","volume":"62","author":"Tang","year":"2020","journal-title":"EBioMedicine"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1111\/den.13688","article-title":"Detecting early gastric cancer: Comparison between the diagnostic ability of convolutional neural networks and endoscopists","volume":"33","author":"Ikenoyama","year":"2021","journal-title":"Dig. Endosc."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/j.gie.2020.06.047","article-title":"Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: Efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging","volume":"92","author":"Nagao","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1016\/j.gie.2017.11.029","article-title":"Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging","volume":"87","author":"Kanesaka","year":"2018","journal-title":"Gastrointest. Endosc."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.gie.2018.11.011","article-title":"Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy","volume":"89","author":"Zhu","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yang, H., and Hu, B. (2021). Diagnosis of Helicobacter pylori Infection and Recent Advances. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081305"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e21983","DOI":"10.2196\/21983","article-title":"Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy","volume":"22","author":"Bang","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.vgie.2020.08.013","article-title":"Artificial intelligence in gastrointestinal endoscopy","volume":"5","author":"Pannala","year":"2020","journal-title":"VideoGIE"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.ebiom.2017.10.014","article-title":"Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images","volume":"25","author":"Shichijo","year":"2017","journal-title":"EBioMedicine"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e00109","DOI":"10.14309\/ctg.0000000000000109","article-title":"High Accuracy of Convolutional Neural Network for Evaluation of Helicobacter pylori Infection Based on Endoscopic Images: Preliminary Experience","volume":"10","author":"Zheng","year":"2019","journal-title":"Clin. Transl. Gastroenterol."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Bordin, D.S., Voynovan, I.N., Andreev, D.N., and Maev, I.V. (2021). Current Helicobacter pylori Diagnostics. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081458"},{"key":"ref_66","first-page":"462","article-title":"Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging-bright and linked color imaging: A single-center prospective study","volume":"31","author":"Hirotaka","year":"2018","journal-title":"Ann. Gastroenterol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1007\/s10120-020-01077-1","article-title":"Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: A single-center prospective study (with video)","volume":"23","author":"Nakashima","year":"2020","journal-title":"Gastric Cancer"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1016\/j.gie.2023.01.007","article-title":"Development and validation of a convolutional neural network model for diagnosing Helicobacter pylori infections with endoscopic images: A multicenter study","volume":"97","author":"Seo","year":"2023","journal-title":"Gastrointest. Endosc."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.dld.2023.02.010","article-title":"Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time","volume":"55","author":"Li","year":"2023","journal-title":"Dig. Liver Dis."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1111\/den.13509","article-title":"Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection","volume":"32","author":"Yasuda","year":"2020","journal-title":"Dig. Endosc."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"E139","DOI":"10.1055\/s-0043-120830","article-title":"Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images","volume":"6","author":"Itoh","year":"2018","journal-title":"Endosc. Int. Open"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/18\/2862\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:29Z","timestamp":1760129189000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/18\/2862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,5]]},"references-count":71,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["diagnostics13182862"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13182862","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,5]]}}}