{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T06:21:12Z","timestamp":1789712472571,"version":"4.0.1"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>We aimed to predict keratoconus progression and the need for corneal crosslinking (CXL) using deep learning (DL). Two hundred and seventy-four corneal tomography images taken by Pentacam HR\u00ae (Oculus, Wetzlar, Germany) of 158 keratoconus patients were examined. All patients were examined two times or more, and divided into two groups; the progression group and the non-progression group. An axial map of the frontal corneal plane, a pachymetry map, and a combination of these two maps at the initial examination were assessed according to the patients\u2019 age. Training with a convolutional neural network on these learning data objects was conducted. Ninety eyes showed progression and 184 eyes showed no progression. The axial map, the pachymetry map, and their combination combined with patients\u2019 age showed mean AUC values of 0.783, 0.784, and 0.814 (95% confidence interval (0.721\u20130.845) (0.722\u20130.846), and (0.755\u20130.872), respectively), with sensitivities of 87.8%, 77.8%, and 77.8% ((79.2\u201393.7), (67.8\u201385.9), and (67.8\u201385.9)) and specificities of 59.8%, 65.8%, and 69.6% ((52.3\u201366.9), (58.4\u201372.6), and (62.4\u201376.1)), respectively. Using the proposed DL neural network model, keratoconus progression can be predicted on corneal tomography maps combined with patients\u2019 age.<\/jats:p>","DOI":"10.3390\/jcm10040844","type":"journal-article","created":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T20:56:21Z","timestamp":1613681781000},"page":"844","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6670-2043","authenticated-orcid":false,"given":"Naoko","family":"Kato","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroki","family":"Masumoto","sequence":"additional","affiliation":[{"name":"Department of Technology and Design Thinking for Medicine, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima 734-8551, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mao","family":"Tanabe","sequence":"additional","affiliation":[{"name":"Department of Technology and Design Thinking for Medicine, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima 734-8551, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chikako","family":"Sakai","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2892-9810","authenticated-orcid":false,"given":"Kazuno","family":"Negishi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9533-7404","authenticated-orcid":false,"given":"Hidemasa","family":"Torii","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hitoshi","family":"Tabuchi","sequence":"additional","affiliation":[{"name":"Department of Technology and Design Thinking for Medicine, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima 734-8551, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8874-7111","authenticated-orcid":false,"given":"Kazuo","family":"Tsubota","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan"},{"name":"Tsubota Laboratory, Inc., Tokyo 160-0016, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/S0002-9394(02)02220-1","article-title":"Riboflavin\/ultraviolet-a\u2013induced collagen crosslinking for the treatment of keratoconus","volume":"135","author":"Wollensak","year":"2003","journal-title":"Am. 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