{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T13:07:46Z","timestamp":1788354466510,"version":"build-2803163510"},"reference-count":25,"publisher":"Georg Thieme Verlag KG","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Endoscopy"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2002We previously reported for the first time the usefulness of artificial intelligence (AI) systems in detecting gastric cancers. However, the \u201coriginal convolutional neural network (O-CNN)\u201d employed in the previous study had a relatively low positive predictive value (PPV). Therefore, we aimed to develop an advanced AI-based diagnostic system and evaluate its applicability for the classification of gastric cancers and gastric ulcers.<\/jats:p><jats:p>\n          Methods\u2002We constructed an \u201cadvanced CNN\u201d (A-CNN) by adding a new training dataset (4453 gastric ulcer images from 1172 lesions) to the O-CNN, which had been trained using 13 584 gastric cancer and 373 gastric ulcer images. The diagnostic performance of the A-CNN in terms of classifying gastric cancers and ulcers was retrospectively evaluated using an independent validation dataset (739 images from 100 early gastric cancers and 720 images from 120 gastric ulcers) and compared with that of the O-CNN by estimating the overall classification accuracy. <\/jats:p><jats:p>\n          Results\u2002The sensitivity, specificity, and PPV of the A-CNN in classifying gastric cancer at the lesion level were 99.0\u200a% (95\u200a% confidence interval [CI] 94.6\u200a%\u2212100\u200a%), 93.3\u200a% (95\u200a%CI 87.3\u200a%\u221297.1\u200a%), and 92.5\u200a% (95\u200a%CI 85.8\u200a%\u221296.7\u200a%), respectively, and for classifying gastric ulcers were 93.3\u200a% (95\u200a%CI 87.3\u200a%\u221297.1\u200a%), 99.0\u200a% (95\u200a%CI 94.6\u200a%\u2212100\u200a%), and 99.1\u200a% (95\u200a%CI 95.2\u200a%\u2212100\u200a%), respectively. At the lesion level, the overall accuracies of the O- and A-CNN for classifying gastric cancers and gastric ulcers were 45.9\u200a% (gastric cancers 100\u200a%, gastric ulcers 0.8\u200a%) and 95.9\u200a% (gastric cancers 99.0\u200a%, gastric ulcers 93.3\u200a%), respectively.<\/jats:p><jats:p>\n          Conclusion\u2002The newly developed AI-based diagnostic system can effectively classify gastric cancers and gastric ulcers.<\/jats:p>","DOI":"10.1055\/a-1194-8771","type":"journal-article","created":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T23:40:48Z","timestamp":1591400448000},"page":"1077-1083","source":"Crossref","is-referenced-by-count":48,"title":["Artificial intelligence-based diagnostic system classifying gastric cancers and ulcers: comparison between the original and newly developed systems"],"prefix":"10.1055","volume":"52","clinical-trial-number":[{"clinical-trial-number":"jma-iia00283","registry":"10.18810\/jma"}],"author":[{"given":"Ken","family":"Namikawa","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshiaki","family":"Hirasawa","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan"},{"name":"Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaoru","family":"Nakano","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan"},{"name":"Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yohei","family":"Ikenoyama","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitsuaki","family":"Ishioka","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sho","family":"Shiroma","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshitaka","family":"Tokai","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Cancer 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