{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T01:37:41Z","timestamp":1790300261707,"version":"4.1.0"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CNPq (National Council for Scientific and Technological Development)","award":["144167\/2020-4"],"award-info":[{"award-number":["144167\/2020-4"]}]},{"name":"CNPq (National Council for Scientific and Technological Development)","award":["303854\/2022-7"],"award-info":[{"award-number":["303854\/2022-7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vision"],"abstract":"<jats:p>Recent advances in artificial intelligence (AI) have transformed ophthalmic diagnostics, particularly for retinal diseases. In this prospective, non-randomized study, we evaluated the performance of an AI-based software system against conventional clinical assessment\u2014both quantitative and qualitative\u2014of optical coherence tomography (OCT) images for diagnosing diabetic macular edema (DME). A total of 700 OCT exams were analyzed across 26 features, including demographic data (age, sex), eye laterality, visual acuity, and 21 quantitative OCT parameters (Macula Map A X-Y). We tested two classification scenarios: binary (DME presence vs. absence) and multiclass (six distinct DME phenotypes). To streamline feature selection, we applied paraconsistent feature engineering (PFE), isolating the most diagnostically relevant variables. We then compared the diagnostic accuracies of logistic regression, support vector machines (SVM), K-nearest neighbors (KNN), and decision tree models. In the binary classification using all features, SVM and KNN achieved 92% accuracy, while logistic regression reached 91%. When restricted to the four PFE-selected features, accuracy modestly declined to 84% for both logistic regression and SVM. These findings underscore the potential of AI\u2014and particularly PFE\u2014as an efficient, accurate aid for DME screening and diagnosis.<\/jats:p>","DOI":"10.3390\/vision9030075","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T13:01:13Z","timestamp":1756818073000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Comparative Study Between Clinical Optical Coherence Tomography (OCT) Analysis and Artificial Intelligence-Based Quantitative Evaluation in the Diagnosis of Diabetic Macular Edema"],"prefix":"10.3390","volume":"9","author":[{"given":"Camila","family":"Brand\u00e3o Fantozzi","sequence":"first","affiliation":[{"name":"Escola T\u00e9cnica Estadual \u201cPhiladelpho Gouv\u00eaa Netto\u201d, S\u00e3o Jos\u00e9 do Rio Preto 15035-010, SP, Brazil"},{"name":"Faculdade de Medicina de S\u00e3o Jos\u00e9 do Rio Preto, S\u00e3o Jos\u00e9 do Rio Preto 15090-000, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Let\u00edcia Margaria","family":"Peres","sequence":"additional","affiliation":[{"name":"Faculdade de Medicina de S\u00e3o Jos\u00e9 do Rio Preto, S\u00e3o Jos\u00e9 do Rio Preto 15090-000, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jogi Suda","family":"Neto","sequence":"additional","affiliation":[{"name":"Instituto de Bioci\u00eancias, Letras e Ci\u00eancias Exatas, Universidade Estadual Paulista \u201cJ\u00falio de Mesquita Filho\u201d, S\u00e3o Jos\u00e9 do Rio Preto 15054-000, SP, Brazil"},{"name":"European Organization for Nuclear Research (CERN), 1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4836-3113","authenticated-orcid":false,"given":"Cinara C\u00e1ssia","family":"Brand\u00e3o","sequence":"additional","affiliation":[{"name":"Faculdade de Medicina de S\u00e3o Jos\u00e9 do Rio Preto, S\u00e3o Jos\u00e9 do Rio Preto 15090-000, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodrigo Capobianco","family":"Guido","sequence":"additional","affiliation":[{"name":"Instituto de Bioci\u00eancias, Letras e Ci\u00eancias Exatas, Universidade Estadual Paulista \u201cJ\u00falio de Mesquita Filho\u201d, S\u00e3o Jos\u00e9 do Rio Preto 15054-000, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rubens Camargo","family":"Siqueira","sequence":"additional","affiliation":[{"name":"Faculdade de Medicina de S\u00e3o Jos\u00e9 do Rio Preto, S\u00e3o Jos\u00e9 do Rio Preto 15090-000, SP, Brazil"},{"name":"Centro de Pesquisa Rubens Siqueira, S\u00e3o Jos\u00e9 do Rio Preto 15010-100, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.survophthal.2018.09.002","article-title":"The Current State of Artificial Intelligence in Ophthalmology","volume":"64","author":"Kapoor","year":"2019","journal-title":"Surv. 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