In:
PLOS Digital Health, Public Library of Science (PLoS), Vol. 1, No. 10 ( 2022-10-20), p. e0000112-
Abstract:
People with COVID-19 can experience impairing symptoms that require enhanced surveillance. Our objective was to train an artificial intelligence-based model to predict the presence of COVID-19 symptoms and derive a digital vocal biomarker for easily and quantitatively monitoring symptom resolution. We used data from 272 participants in the prospective Predi-COVID cohort study recruited between May 2020 and May 2021. A total of 6473 voice features were derived from recordings of participants reading a standardized pre-specified text. Models were trained separately for Android devices and iOS devices. A binary outcome (symptomatic versus asymptomatic) was considered, based on a list of 14 frequent COVID-19 related symptoms. A total of 1775 audio recordings were analyzed (6.5 recordings per participant on average), including 1049 corresponding to symptomatic cases and 726 to asymptomatic ones. The best performances were obtained from Support Vector Machine models for both audio formats. We observed an elevated predictive capacity for both Android (AUC = 0.92, balanced accuracy = 0.83) and iOS (AUC = 0.85, balanced accuracy = 0.77) as well as low Brier scores (0.11 and 0.16 respectively for Android and iOS when assessing calibration. The vocal biomarker derived from the predictive models accurately discriminated asymptomatic from symptomatic individuals with COVID-19 (t-test P-values 〈 0.001). In this prospective cohort study, we have demonstrated that using a simple, reproducible task of reading a standardized pre-specified text of 25 seconds enabled us to derive a vocal biomarker for monitoring the resolution of COVID-19 related symptoms with high accuracy and calibration.
Type of Medium:
Online Resource
ISSN:
2767-3170
DOI:
10.1371/journal.pdig.0000112
DOI:
10.1371/journal.pdig.0000112.g001
DOI:
10.1371/journal.pdig.0000112.g002
DOI:
10.1371/journal.pdig.0000112.t001
DOI:
10.1371/journal.pdig.0000112.t002
DOI:
10.1371/journal.pdig.0000112.t003
DOI:
10.1371/journal.pdig.0000112.s001
DOI:
10.1371/journal.pdig.0000112.s002
DOI:
10.1371/journal.pdig.0000112.s003
DOI:
10.1371/journal.pdig.0000112.s004
DOI:
10.1371/journal.pdig.0000112.s005
DOI:
10.1371/journal.pdig.0000112.s006
DOI:
10.1371/journal.pdig.0000112.s007
DOI:
10.1371/journal.pdig.0000112.r001
DOI:
10.1371/journal.pdig.0000112.r002
DOI:
10.1371/journal.pdig.0000112.r003
DOI:
10.1371/journal.pdig.0000112.r004
DOI:
10.1371/journal.pdig.0000112.r005
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
3106944-7
Permalink