{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T20:33:06Z","timestamp":1775680386257,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T00:00:00Z","timestamp":1653091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["PTDC\/EEI-EEE\/5557\/2020"],"award-info":[{"award-number":["PTDC\/EEI-EEE\/5557\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Stomach cancer is the third deadliest type of cancer in the world (0.86 million deaths in 2017). In 2035, a 20% increase will be observed both in incidence and mortality due to demographic effects if no interventions are foreseen. Upper GI endoscopy (UGIE) plays a paramount role in early diagnosis and, therefore, improved survival rates. On the other hand, human and technical factors can contribute to misdiagnosis while performing UGIE. In this scenario, artificial intelligence (AI) has recently shown its potential in compensating for the pitfalls of UGIE, by leveraging deep learning architectures able to efficiently recognize endoscopic patterns from UGIE video data. This work presents a review of the current state-of-the-art algorithms in the application of AI to gastroscopy. It focuses specifically on the threefold tasks of assuring exam completeness (i.e., detecting the presence of blind spots) and assisting in the detection and characterization of clinical findings, both gastric precancerous conditions and neoplastic lesion changes. Early and promising results have already been obtained using well-known deep learning architectures for computer vision, but many algorithmic challenges remain in achieving the vision of AI-assisted UGIE. Future challenges in the roadmap for the effective integration of AI tools within the UGIE clinical practice are discussed, namely the adoption of more robust deep learning architectures and methods able to embed domain knowledge into image\/video classifiers as well as the availability of large, annotated datasets.<\/jats:p>","DOI":"10.3390\/diagnostics12051278","type":"journal-article","created":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T09:18:08Z","timestamp":1653124688000},"page":"1278","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice"],"prefix":"10.3390","volume":"12","author":[{"given":"Francesco","family":"Renna","sequence":"first","affiliation":[{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia, 3200-465 Porto, Portugal"},{"name":"Faculdade de Ci\u00eancias, Universidade do Porto, 4169-007 Porto, Portugal"}]},{"given":"Miguel","family":"Martins","sequence":"additional","affiliation":[{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia, 3200-465 Porto, Portugal"},{"name":"Faculdade de Ci\u00eancias, Universidade do Porto, 4169-007 Porto, Portugal"}]},{"given":"Alexandre","family":"Neto","sequence":"additional","affiliation":[{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia, 3200-465 Porto, Portugal"},{"name":"Escola de Ci\u00eancias e Tecnologia, Universidade de Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7693","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Cunha","sequence":"additional","affiliation":[{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia, 3200-465 Porto, Portugal"},{"name":"Escola de Ci\u00eancias e Tecnologia, Universidade de Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal"}]},{"given":"Diogo","family":"Lib\u00e2nio","sequence":"additional","affiliation":[{"name":"Departamento de Ci\u00eancias da Informa\u00e7\u00e3o e da Decis\u00e3o em Sa\u00fade\/Centro de Investiga\u00e7\u00e3o em Tecnologias e Servi\u00e7os de Sa\u00fade (CIDES\/CINTESIS), Faculdade de Medicina, Universidade do Porto, 4200-319 Porto, Portugal"}]},{"given":"M\u00e1rio","family":"Dinis-Ribeiro","sequence":"additional","affiliation":[{"name":"Departamento de Ci\u00eancias da Informa\u00e7\u00e3o e da Decis\u00e3o em Sa\u00fade\/Centro de Investiga\u00e7\u00e3o em Tecnologias e Servi\u00e7os de Sa\u00fade (CIDES\/CINTESIS), Faculdade de Medicina, Universidade do Porto, 4200-319 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7501-6523","authenticated-orcid":false,"given":"Miguel","family":"Coimbra","sequence":"additional","affiliation":[{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia, 3200-465 Porto, Portugal"},{"name":"Faculdade de Ci\u00eancias, Universidade do Porto, 4169-007 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1016\/S0140-6736(17)32154-2","article-title":"Global, Regional, and National Incidence, Prevalence, and Years Lived with Disability for 328 Diseases and Injuries for 195 Countries, 1990\u20132016: A Systematic Analysis for the Global Burden of Disease Study 2016","volume":"390","author":"Vos","year":"2017","journal-title":"Lancet"},{"key":"ref_2","unstructured":"(2022, March 31). 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