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Machine learning aided automated differential diagnostics of chronic rhinitis based on optical coherence tomography

Chronic rhinitis (CR) is among the most frequent inflammatory diseases of ear-nose-throat (ENT) covering up to 30% of the population. Different forms of CR require different treatment tactics, which indicates the need for an efficient tool for differential diagnostics of CR. Optical coherence tomography (OCT) is a promising tool for fast non-invasive evaluation of nasal mucosa, which, however, requires further interpretation of the obtained diagnostic image. In this paper, we provide a comparative analysis of several machine learning approaches that aim at automated differential diagnostics of CR based on diagnostic OCT images of 78 patients aged between 28 and 74 ages. Gradient boosting decision trees (GBT) approach reveals the best classification accuracy (98% and 94% for binary and diagnostic classification, respectively). It shows that proposed approaches have potential for automated classification of CR OCT images.