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Standard ENT inspection approaches indicate low sensitivity in detecting maxillary sinus pathologies. In this paper, we report on capabilities of digital diaphanoscopy combined with machine learning tools in the detection of such pathologies. We provide a comparative analysis of two machine learning approaches applied to digital diapahnoscopy data, namely, convolutional neural networks and linear discriminant analysis. The sensitivity and specificity values obtained for both employed approaches exceed the reported accuracy indicators for traditional screening diagnosis methods (such as nasal endoscopy or ultrasound), suggesting the prospects of their usage for screening maxillary sinuses alterations. The analysis of the obtained values showed that the linear discriminant analysis, being a simpler approach as compared to neural networks, allows one to detect the maxillary sinus pathologies with the sensitivity and specificity of 0.88 and 0.98, respectively.<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/jbio202300138-gra-0001.png\" xlink:title=\"image\"\/><\/jats:p>","DOI":"10.1002\/jbio.202300138","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T03:00:00Z","timestamp":1686106800000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2950-4443","authenticated-orcid":false,"given":"Ekaterina O.","family":"Bryanskaya","sequence":"first","affiliation":[{"name":"Research and Development Center of Biomedical Photonics Orel State University  Orel Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6974-3505","authenticated-orcid":false,"given":"Viktor V.","family":"Dremin","sequence":"additional","affiliation":[{"name":"Research and Development Center of Biomedical Photonics Orel State University  Orel Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0024-8518","authenticated-orcid":false,"given":"Valery V.","family":"Shupletsov","sequence":"additional","affiliation":[{"name":"Research and Development Center of Biomedical Photonics Orel State University  Orel Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5121-6045","authenticated-orcid":false,"given":"Alexey V.","family":"Kornaev","sequence":"additional","affiliation":[{"name":"Research Center for Artificial Intelligence Innopolis University  Innopolis Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6804-6369","authenticated-orcid":false,"given":"Mikhail Yu.","family":"Kirillin","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics RAS  Nizhny Novgorod Russia"},{"name":"N.I. 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