Machine-learned pattern identification in olfactory subtest results

  • The human sense of smell is often analyzed as being composed of three main components comprising olfactory threshold, odor discrimination and the ability to identify odors. A relevant distinction of the three components and their differential changes in distinct disorders remains a research focus. The present data-driven analysis aimed at establishing a cluster structure in the pattern of olfactory subtest results. Therefore, unsupervised machine-learning was applied onto olfactory subtest results acquired in 10,714 subjects with nine different olfactory pathologies. Using the U-matrix, Emergent Self-organizing feature maps (ESOM) identified three different clusters characterized by (i) low threshold and good discrimination and identification, (ii) very high threshold associated with absent to poor discrimination and identification ability, or (iii) medium threshold, i.e., in the mid-range of possible thresholds, associated with reduced discrimination and identification ability. Specific etiologies of olfactory (dys)function were unequally represented in the clusters (p < 2.2 · 10−16). Patients with congenital anosmia were overrepresented in the second cluster while subjects with postinfectious olfactory dysfunction belonged frequently to the third cluster. However, the clusters provided no clear separation between etiologies. Hence, the present verification of a distinct cluster structure encourages continued scientific efforts at olfactory test pattern recognition.

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Author:Jörn LötschORCiDGND, Thomas Hummel, Alfred Ultsch
Pubmed Id:
Parent Title (English):Scientific reports
Publisher:Macmillan Publishers Limited, part of Springer Nature
Place of publication:[London]
Document Type:Article
Year of Completion:2016
Date of first Publication:2016/10/20
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/06/07
Tag:Functional clustering; Olfactory system
Issue:Art. 35688
Page Number:8
First Page:1
Last Page:8
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Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 4.0