Sorting of odor dilutions is a meaningful addition to assessments of olfactory function as suggested by machine-learning-based analyses
- Background: The categorization of individuals as normosmic, hyposmic, or anosmic from test results of odor threshold, discrimination, and identification may provide a limited view of the sense of smell. The purpose of this study was to expand the clinical diagnostic repertoire by including additional tests. Methods: A random cohort of n = 135 individuals (83 women and 52 men, aged 21 to 94 years) was tested for odor threshold, discrimination, and identification, plus a distance test, in which the odor of peanut butter is perceived, a sorting task of odor dilutions for phenylethyl alcohol and eugenol, a discrimination test for odorant enantiomers, a lateralization test with eucalyptol, a threshold assessment after 10 min of exposure to phenylethyl alcohol, and a questionnaire on the importance of olfaction. Unsupervised methods were used to detect structure in the olfaction-related data, followed by supervised feature selection methods from statistics and machine learning to identify relevant variables. Results: The structure in the olfaction-related data divided the cohort into two distinct clusters with n = 80 and 55 subjects. Odor threshold, discrimination, and identification did not play a relevant role for cluster assignment, which, on the other hand, depended on performance in the two odor dilution sorting tasks, from which cluster assignment was possible with a median 100-fold cross-validated balanced accuracy of 77–88%. Conclusions: The addition of an odor sorting task with the two proposed odor dilutions to the odor test battery expands the phenotype of olfaction and fits seamlessly into the sensory focus of standard test batteries.
Author: | Jörn LötschORCiDGND, Anne Huster, Thomas HummelORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-755673 |
DOI: | https://doi.org/10.3390/jcm11144012 |
ISSN: | 2077-0383 |
Parent Title (English): | Journal of Clinical Medicine |
Publisher: | MDPI |
Place of publication: | Basel |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2022/07/11 |
Date of first Publication: | 2022/07/11 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2023/09/11 |
Tag: | data science; machine learning; olfaction; olfactory testing; patients |
Volume: | 11 |
Issue: | 14, art. 4012 |
Article Number: | 4012 |
Page Number: | 23 |
First Page: | 1 |
Last Page: | 23 |
Note: | Gefördert durch den Open-Access-Publikationsfonds der Goethe-Universität |
Note: | Funding: Deutsche Forschungsgemeinschaft ; DFG LO 612/16-1 |
Note: | Funding: SMWK/TUD ; EXUN2019 |
Note: | Data Availability Statement Data available on request from the senior author. Parts of the Python code created for data analysis are available at https://github.com/JornLotsch/OdorSortingReport (accessed on 28 January 2022). |
HeBIS-PPN: | 514232226 |
Institutes: | Medizin |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit | |
Sammlungen: | Universitätspublikationen |
Open-Access-Publikationsfonds: | Medizin |
Licence (German): | Creative Commons - Namensnennung 3.0 |