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Related Concept Videos

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Updated: Sep 21, 2025

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Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep

Ting Dang1, Jing Han1, Tong Xia1

  • 1Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.

Journal of Medical Internet Research
|June 2, 2022
PubMed
Summary
This summary is machine-generated.

This study shows that analyzing longitudinal audio data, including cough and breathing sounds, can effectively monitor COVID-19 progression and recovery. This audio-based tracking tool offers a flexible and affordable method for disease monitoring.

Keywords:
COVID-19COVID-19 progressionaudiodeep learninglongitudinal studymobile health

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Area of Science:

  • Digital Health
  • Biomedical Signal Processing
  • Machine Learning in Healthcare

Background:

  • Current COVID-19 audio analysis focuses on one-off detection, neglecting disease progression and recovery monitoring.
  • Longitudinal tracking of COVID-19 progression via audio biomarkers can provide critical insights for timely treatment adjustments and healthcare resource management.

Purpose of the Study:

  • To explore the potential of sequential deep learning techniques for predicting COVID-19 progression and recovery trends using longitudinal audio samples.
  • To develop and validate a deep learning-enabled tool for continuous COVID-19 monitoring.

Main Methods:

  • Crowdsourced respiratory audio data (cough, breathing, voice) from 212 individuals over 5-385 days were analyzed alongside COVID-19 test results.
  • A deep learning model utilizing gated recurrent units (GRUs) was developed to track audio biomarker dynamics for disease progression prediction.
  • The study involved COVID-19 detection and longitudinal disease progression prediction, evaluating performance using AUROC, sensitivity, specificity, and correlation coefficients.

Main Results:

  • Longitudinal audio biomarker analysis demonstrated effectiveness in COVID-19 detection, achieving an AUROC of 0.79, sensitivity of 0.75, and specificity of 0.71.
  • The predicted disease progression trajectory showed high consistency with longitudinal test results (correlation of 0.75 in the test cohort and 0.86 in a recovery subset).
  • Findings indicate that monitoring COVID-19 evolution through longitudinal audio data is promising for tracking disease progression and recovery.

Conclusions:

  • An audio-based system using deep learning effectively monitors COVID-19 progression and recovery trends, showing high consistency with test results.
  • This technology holds potential for post-pandemic healthcare, aiding treatment guidance and resource allocation.
  • The framework demonstrates the applicability of telemonitoring for respiratory diseases, offering a flexible, affordable, and timely solution.