Maxillary sinus pathologies remain among the most common ENT diseases requiring timely diagnosis for successful treatment. 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.
Bibliographical noteCopyright © 2023 Wiley-VCH GmbH. This is the peer reviewed version of the following article: "Bryanskaya, E.O. et al. (2023) ‘Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning’, Journal of Biophotonics," which has been published in final form at https://doi.org/10.1002/jbio.202300138. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Acknowledgements: This study was funded by RFBR according to the research project No. 20-32-90147 and by FASIE according to the project No. 353ГС1ЦТС10-D5/80270. Thanks to the volunteers and patients of the University Clinic of the Yevdokimov A.I. Moscow State University of Medicine and Dentistry (Moscow, Russia).
- convolutional neural networks
- digital diaphanoscopy
- linear discriminant analysis
- maxillary sinuses
- optical diagnostics