TY  - JOUR
A1  - Lötsch, Jörn
A1  - Hummel, Thomas
A1  - Ultsch, Alfred
T1  - Machine-learned pattern identification in olfactory subtest results
T2  - Scientific reports
N2  - 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.
KW  - Functional clustering
KW  - Olfactory system
Y1  - 2016
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/46567
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-465673
SN  - 2045-2322
N1  - This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
VL  - 6
IS  - Art. 35688
SP  - 1
EP  - 8
PB  - Macmillan Publishers Limited, part of Springer Nature
CY  - [London]
ER  -