A Unifying Data-Driven Model of Human Olfactory Pathology Representing Known Etiologies of Dysfunction

Chem Senses. 2016 Nov 1;41(9):763-770. doi: 10.1093/chemse/bjw089.


In the clinical diagnosis of olfactory function, 2 quantitative extremes of either lost or normal olfactory function are in the focus while no particular attention is directed at the interval between the 2 main diagnoses of "anosmia" or "normosmia", respectively. We analyzed the modal distribution of olfactory scores with the intention to describe a complex human olfactory pathology in a unifying model. In a cross-sectional retrospective study, olfactory performance scores acquired from 10714 individuals by means of a clinically established psychophysical test were analyzed with respect to their modal distribution by fitting a Gaussian mixture model (GMM) to the data. The probability distribution of all olfactory scores was found to be multimodal. It could be described as a mixture of 6 Gaussian distributions at a high statistical significance level of P < 10 -5 . Moreover, 9 different pathologies associated with the olfactory dysfunction could be shown to be reflected in 1-3 distinct Gaussians. This provides the possibility to assign distinct degrees of olfactory acuity with each etiology. Results indicate that human olfactory pathology is composed of clearly distinct subpathologies that can be connected with underlying subetiologies. We present a unifying data science-based model that satisfies the human olfactory pathology observed in 10714 subjects. The analysis of the distribution of their olfactory performance scores suggests a complex but very distinct human olfactory pathology. This implies a distinction of the olfactory diagnosis of hyposmia from those of anosmia or normosmia.

Keywords: bioinformatics; data science; human olfactory pathology; olfactory scores.