• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. Machine-learned pattern identification in olfactory subtest results
 
  • Details
  • Full
Options
2016
Journal Article
Title

Machine-learned pattern identification in olfactory subtest results

Abstract
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.
Author(s)
Lötsch, J.
Hummel, T.
Ultsch, A.
Journal
Scientific Reports  
Open Access
Link
Link
DOI
10.1038/srep35688
Additional full text version
Landing Page
Language
English
Fraunhofer-Institut für Molekularbiologie und Angewandte Oekologie IME  
  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024