{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T03:02:54Z","timestamp":1771038174150,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"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>Glaucoma is a chronic, progressive eye disease affecting the optic nerve, which may cause visual damage and blindness. In this study, we present a machine-learning investigation to classify patients with glaucoma (case group) with respect to normal participants (control group). We examined 172 eyes at the Ophthalmology Clinic of the \u201cElpis\u201d General Hospital of Athens between October 2022 and September 2023. In addition, we investigated the glaucoma classification in terms of the following: (a) eye selection and (b) gender. Our methodology was based on the features extracted via two diagnostic optical systems: (i) conventional optical coherence tomography (OCT) and (ii) a modern RETeval portable device. The machine-learning approach comprised three different classifiers: the Bayesian, the Probabilistic Neural Network (PNN), and Support Vectors Machines (SVMs). For all cases examined, classification accuracy was found to be significantly higher when using the RETeval device with respect to the OCT system, as follows: 14.7% for all participants, 13.4% and 29.3% for eye selection (right and left, respectively), and 25.6% and 22.6% for gender (male and female, respectively). The most efficient classifier was found to be the SVM compared to the PNN and Bayesian classifiers. In summary, all aforementioned comparisons demonstrate that the RETeval device has the advantage over the OCT system for the classification of glaucoma patients by using the machine-learning approach.<\/jats:p>","DOI":"10.3390\/diagnostics14040349","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T03:22:31Z","timestamp":1707189751000},"page":"349","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device"],"prefix":"10.3390","volume":"14","author":[{"given":"Marsida","family":"Bekollari","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Dettoraki","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, \u201cElpis\u201d General Hospital, 11522 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valentina","family":"Stavrou","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, \u201cElpis\u201d General Hospital, 11522 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3252-4055","authenticated-orcid":false,"given":"Dimitris","family":"Glotsos","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3036-9389","authenticated-orcid":false,"given":"Panagiotis","family":"Liaparinos","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/0893-6080(90)90049-Q","article-title":"Probabilistic Neural Networks","volume":"3","author":"Specht","year":"1990","journal-title":"Neural Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_4","unstructured":"Theodoridis, S., and Koutroumbas, K. (2008). Pattern Recognition, Academic Press. 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