Classification of maxillary sinus states according to digital diaphanoscopy with the use of machine learning

Currently, the issue of diagnosis of pathological changes in the maxillary sinus (MS) is an urgent task. The digital diaphanoscopy method, which allows to visualize the tissues of sinuses through the use of probing optical radiation in red and near-infrared ranges, seems promising for the early diagnosis of this pathology. However, there is a need to improve the accuracy of this method, reduce the time of study and simplify the process of recorded images (diaphanograms) classification by creating a medical decision making support system (MDMSS). Objective. To develop a MDMSS for classification of diaphanograms of digital diaphanoscopy on the basis of a convolutional neural network (CNN). Patients and methods. The study involved 80 healthy volunteers and 76 patients with MS pathology. Diaphanograms were recorded using a digital diaphanoscopy device at two probing wavelengths (650 and 850 nm). Analysis of diaphanograms (160 diaphanograms of conditionally healthy volunteers, 78 diaphanograms of patients with sinusitis and 32 diphanograms of patients with MS cyst) was carried out using a developed image classification model based on ResNet-50 CNN. Results. High accuracy values (sensitivity of 0.95 and specificity of 0.88), which exceeded all previously proposed developments based on linear discriminant analysis, were obtained. The problem of MS pathology differentiation into sinusitis and cystic fluid classes was solved by means of developed MDMSS. Conclusion. The developed classification model can be applied for digital diaphanoscopy for the purpose of early detection of MS pathological changes in telemedicine and automated ENT consultations using MDMSS. Analysis of the results showed the need to expand the database for further training of the classification model.