{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T07:18:57Z","timestamp":1785568737309,"version":"3.56.0"},"reference-count":22,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T00:00:00Z","timestamp":1634601600000},"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>The accurate diagnosis of keratoconus, especially in its early stages of development, allows one to utilise timely and proper treatment strategies for slowing the progression of the disease and provide visual rehabilitation. Various keratometry indices and classifications for quantifying the severity of keratoconus have been developed. Today, many of them involve the use of the latest methods of computer processing and data analysis. The main purpose of this work was to develop a machine-learning-based algorithm to precisely determine the stage of keratoconus, allowing optimal management of patients with this disease. A multicentre retrospective study was carried out to obtain a database of patients with keratoconus and to use machine-learning techniques such as principal component analysis and clustering. The created program allows for us to distinguish between a normal state; preclinical keratoconus; and stages 1, 2, 3 and 4 of the disease, with an accuracy in terms of the AUC of 0.95 to 1.00 based on keratotopographer readings, relative to the adapted Amsler\u2013Krumeich algorithm. The predicted stage and additional diagnostic criteria were then used to create a standardised keratoconus management algorithm. We also developed a web-based interface for the algorithm, providing us the opportunity to use the software in a clinical environment.<\/jats:p>","DOI":"10.3390\/diagnostics11101933","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T01:23:46Z","timestamp":1634693026000},"page":"1933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Keratoconus Diagnostic and Treatment Algorithms Based on Machine-Learning Methods"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5666-3493","authenticated-orcid":false,"given":"Boris","family":"Malyugin","sequence":"first","affiliation":[{"name":"S.N. Fyodorov Eye Microsurgery Complex Federal State Institution, 127-486 Moscow, Russia"},{"name":"Faculty of Medicine, A. Yevdokimov Moscow State University of Medicine and Dentistry, 127-473 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2100-2972","authenticated-orcid":false,"given":"Sergej","family":"Sakhnov","sequence":"additional","affiliation":[{"name":"S.N. Fyodorov Eye Microsurgery Complex Federal State Institution, 350-012 Krasnodar, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3516-1774","authenticated-orcid":false,"given":"Svetlana","family":"Izmailova","sequence":"additional","affiliation":[{"name":"S.N. Fyodorov Eye Microsurgery Complex Federal State Institution, 127-486 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7413-7478","authenticated-orcid":false,"given":"Ernest","family":"Boiko","sequence":"additional","affiliation":[{"name":"S.N. Fyodorov Eye Microsurgery Complex Federal State Institution, 192-283 Saint-Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3637-3645","authenticated-orcid":false,"given":"Nadezhda","family":"Pozdeyeva","sequence":"additional","affiliation":[{"name":"S.N. Fyodorov Eye Microsurgery Complex Federal State Institution, 428-027 Cheboksary, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0885-1355","authenticated-orcid":false,"given":"Lyubov","family":"Axenova","sequence":"additional","affiliation":[{"name":"S.N. Fyodorov Eye Microsurgery Complex Federal State Institution, 350-012 Krasnodar, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5391-5229","authenticated-orcid":false,"given":"Kirill","family":"Axenov","sequence":"additional","affiliation":[{"name":"Fast Lane, 197136 Saint-Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6847-4737","authenticated-orcid":false,"given":"Aleksej","family":"Titov","sequence":"additional","affiliation":[{"name":"S.N. Fyodorov Eye Microsurgery Complex Federal State Institution, 192-283 Saint-Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7719-8778","authenticated-orcid":false,"given":"Anna","family":"Terentyeva","sequence":"additional","affiliation":[{"name":"S.N. Fyodorov Eye Microsurgery Complex Federal State Institution, 428-027 Cheboksary, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3503-6009","authenticated-orcid":false,"given":"Tamriko","family":"Zakaraiia","sequence":"additional","affiliation":[{"name":"S.N. Fyodorov Eye Microsurgery Complex Federal State Institution, 350-012 Krasnodar, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1748-7962","authenticated-orcid":false,"given":"Viktoriya","family":"Myasnikova","sequence":"additional","affiliation":[{"name":"S.N. Fyodorov Eye Microsurgery Complex Federal State Institution, 350-012 Krasnodar, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,19]]},"reference":[{"key":"ref_1","unstructured":"George, A., and Kaufman, E.J. (2021). Keratoconus. StatPearls, StatPearls Publishing. 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