Diagnosed and subjectively perceived long-term effects of COVID-19 infection on olfactory function assessed by supervised machine learning

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung



Loss of olfactory function is a typical acute coronavirus disease 2019 (COVID-19) symptom, at least in early variants of SARS-CoV2.The time that has elapsed since the emergence of COVID-19 now allows for assessing the long-term prognosis of its olfactory impact. Participants (n = 722) of whom n = 464 reported having had COVID-19 dating back with a mode of 174 days were approached in a museum as a relatively unbiased environment. Olfactory function was diagnosed by assessing odor threshold and odor identification performance. Subjects also rated their actual olfactory function on an 11-point numerical scale [0,…10]. Neither the frequency of olfactory diagnostic categories nor olfactory test scores showed any COVID-19-related effects. Olfactory diagnostic categories (anosmia, hyposmia, or normosmia) were similarly distributed among former patients and controls (0.86%, 18.97%, and 80.17% for former patients and 1.17%, 17.51%, and 81.32% for controls). Former COVID-19 patients, however, showed differences in their subjective perception of their own olfactory function.The impact of this effect was substantial enough that supervised machine learning algorithms detected past COVID-19 infections in new subjects, based on reduced self-awareness of olfactory performance and parosmia, while the diagnosed olfactory function did not contribute any relevant information in this context. Based on diagnosed olfactory function, results suggest a positive prognosis for COVID-19-related olfactory loss in the long term.Traces of former infection are found in self-perceptions of olfaction, highlighting the importance of investigating the long-term effects of COVID-19 using reliable and validated diagnostic measures in olfactory testing.


FachzeitschriftChemical senses
PublikationsstatusVeröffentlicht - 1 Jan. 2024

Externe IDs

PubMed 38213039
ORCID /0000-0001-9713-0183/work/153655082
ORCID /0000-0003-1311-8000/work/158767605



  • COVID-19, data science, deep neural networks, human olfaction, machine learning, olfactory testing, Anosmia/diagnosis, Humans, Smell, SARS-CoV-2, RNA, Viral, Olfaction Disorders/diagnosis, Supervised Machine Learning