{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T04:09:32Z","timestamp":1778731772894,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"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 diagnosis of oral lichen planus (OLP) poses many challenges due to its nonspecific clinical symptoms and histopathological features. Therefore, the diagnostic process should include a thorough clinical history, immunological tests, and histopathology. Our study aimed to enhance the diagnostic accuracy of OLP by integrating direct immunofluorescence (DIF) results with clinical data to develop a multivariate predictive model based on the Artificial Neural Network. Eighty patients were assessed using DIF for various markers (immunoglobulins of classes G, A, and M; complement 3; fibrinogen type 1 and 2) and clinical characteristics such as age, gender, and lesion location. Statistical analysis was performed using machine learning techniques in Statistica 13. The following variables were assessed: gender, age on the day of lesion onset, results of direct immunofluorescence, location of white patches, locations of erosions, treatment history, medications and dietary supplement intake, dental status, smoking status, flossing, and using mouthwash. Four statistically significant variables were selected for machine learning after the initial assessment. The final predictive model, based on neural networks, achieved 85% in the testing sample and 71% accuracy in the validation sample. Significant predictors included stress at onset, white patches under the tongue, and erosions on the mandibular gingiva. In conclusion, while the model shows promise, larger datasets and more comprehensive variables are needed to improve diagnostic accuracy for OLP, highlighting the need for further research and collaborative data collection efforts.<\/jats:p>","DOI":"10.3390\/diagnostics14141525","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T15:17:06Z","timestamp":1721056626000},"page":"1525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Classification-Predictive Model Based on Artificial Neural Network Validated by Histopathology and Direct Immunofluorescence for the Diagnosis of Oral Lichen Planus"],"prefix":"10.3390","volume":"14","author":[{"given":"Katarzyna","family":"Osipowicz","sequence":"first","affiliation":[{"name":"Department of Immunodermatology, Medical University of Warsaw, Koszykowa 82a, 02-008 Warsaw, Poland"},{"name":"Faculty of Health Science, Calisia University, Nowy \u015awiat 4, 62-800 Kalisz, Poland"},{"name":"OT.CO Zdrowie Sp. z o.o., Bartycka 24B\/U1, 00-716 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piotr","family":"Turkowski","sequence":"additional","affiliation":[{"name":"Faculty of Health Science, Calisia University, Nowy \u015awiat 4, 62-800 Kalisz, Poland"},{"name":"OT.CO Zdrowie Sp. z o.o., Bartycka 24B\/U1, 00-716 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Izabela","family":"Zdoli\u0144ska-Malinowska","sequence":"additional","affiliation":[{"name":"Escritor.pl, Jana Paw\u0142a II 27, 00-867 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s00005-013-0261-y","article-title":"Etiopathogenesis of recurrent aphthous stomatitis and the role of immunologic aspects: Literature review","volume":"62","author":"Slebioda","year":"2014","journal-title":"Arch. 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