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The problem is, however, challenging for computer-assisted techniques, because different sites have similar appearances. Additionally, across different patients, site appearance variation may be large and inconsistent. Therefore, according to the British and modified Japanese guidelines, we propose a set of oesophagogastroduodenoscopy (EGD) images to be routinely captured and evaluate its efficiency for deep learning-based classification methods.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>A novel EGD image dataset standardising upper GI endoscopy to several steps is established following landmarks proposed in guidelines and annotated by an expert clinician. To demonstrate the discrimination of proposed landmarks that enable the generation of an automated endoscopic report, we train several deep learning-based classification models utilising the well-annotated images.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>We report results for a clinical dataset composed of 211 patients (comprising a total of 3704 EGD images) acquired during routine upper GI endoscopic examinations. We find close agreement between predicted labels using our method and the ground truth labelled by human experts. We observe the limitation of current static image classification scheme for EGD image classification.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Our study presents a framework for developing automated EGD reports using deep learning. We demonstrate that our method is feasible to address EGD image classification and can lead towards improved performance and additionally qualitatively demonstrate its performance on our dataset.<\/jats:p>\n<\/jats:sec>","DOI":"10.1007\/s11548-020-02148-5","type":"journal-article","created":{"date-parts":[[2020,5,6]],"date-time":"2020-05-06T11:02:47Z","timestamp":1588762967000},"page":"1085-1094","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Deep learning-based anatomical site classification for upper gastrointestinal endoscopy"],"prefix":"10.1007","volume":"15","author":[{"given":"Qi","family":"He","sequence":"first","affiliation":[]},{"given":"Sophia","family":"Bano","sequence":"additional","affiliation":[]},{"given":"Omer F.","family":"Ahmad","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Pietro","family":"Valdastri","sequence":"additional","affiliation":[]},{"given":"Laurence B.","family":"Lovat","sequence":"additional","affiliation":[]},{"given":"Danail","family":"Stoyanov","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4257-7978","authenticated-orcid":false,"given":"Siyang","family":"Zuo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,6]]},"reference":[{"issue":"11","key":"2148_CR1","doi-asserted-by":"publisher","first-page":"1886","DOI":"10.1136\/gutjnl-2017-314109","volume":"66","author":"S Beg","year":"2017","unstructured":"Beg S, Ragunath K, Wyman A, Banks M, Trudgill N, Pritchard MD, Riley S, Anderson J, Griffiths H, Bhandari P, Kaye P, Veitch A (2017) Quality standards in upper gastrointestinal endoscopy: a position statement of the British Society of Gastroenterology (BSG) and Association of Upper Gastrointestinal Surgeons of Great Britain and Ireland (AUGIS). 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