Optical Diagnostics of the Maxillary Sinuses by Digital Diaphanoscopy Technology

The work is devoted to the development of a scientific and technical basis for instrument implementation of a digital diaphanoscopy technology for the diagnosis of maxillary sinus inflammatory diseases taking into account the anatomical features of patients (differences in skin structure, skull bone thickness, and sinus size), the optical properties of exercised tissues, and the age and gender characteristics of patients. The technology is based on visualization and analysis of scattering patterns of low-intensity radiation as it passes through the maxillary sinuses. The article presents the experimental data obtained using the digital diaphanoscopy method and the results of numerical simulation of the optical radiation passage through the study area. The experimental setup has been modernized through the installation of a a device for controlling the LED applicator brightness. The approach proposed may have considerable promise for creating diagnostic criteria for various pathological changes and can be used to assess the differences in the optical and anatomical features of males and females

Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning

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