A voice-based biomarker for monitoring symptom resolution in adults with COVID-19: Findings from the prospective Predi-COVID cohort study

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. Author summary People infected with SARS-CoV-2 may develop different forms of COVID-19 characterized by diverse sets of COVID-19 related symptoms and thus may require personalized care. Among digital technologies, voice analysis is a promising field of research to develop user-friendly, cheap-to-collect, non-invasive vocal biomarkers to facilitate the remote monitoring of patients. Previous attempts have tried to use voice to screen for COVID-19, but so far, little research has been done to develop vocal biomarkers specifically for people living with COVID-19. In the Predi-COVID cohort study, we have been able to identify an accurate vocal biomarker to predict the symptomatic status of people with COVID-19 based on a standardized voice recording task of about 25 seconds, where participants had to read a pre-specified text. Such a vocal biomarker could soon be integrated into clinical practice for rapid screening during a consultation to aid clinicians during anamnesis, or into future telemonitoring solutions and digital devices to help people with COVID-19 or Long COVID.