ArticleNPJ digital medicine2022
Sounds of COVID-19: exploring realistic performance of audio-based digital testing.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Large Language Models for Optimizing Patient Recruitment Decisions in Voice Data Generation Projects.Laryngoscope investigative otolaryngology · 2026Article
- Novel deep learning-based optimization framework for the classification of respiratory diseases using lung sound analysis.Scientific reports · 2026Article
- Non-contact lung disease classification via orthogonal frequency division multiplexing-based passive 6G integrated sensing and communication.Communications medicine · 2026Article
- Chronic obstructive pulmonary disease screening using time-frequency features of self-recorded respiratory sounds.JAMIA open · 2025Article
- A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation.Journal of medical Internet research · 2025Article
- Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection.NPJ digital medicine · 2025Article
- SympCoughNet: symptom assisted audio-based COVID-19 detection.Frontiers in digital health · 2025Article
- Generative AI and unstructured audio data for precision public health.npj health systems · 2025Article
- The sound of surgery-development of an acoustic trocar system enabling laparoscopic sound analysis.International journal of computer assisted radiology and surgery · 2024Article
- Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications.Plants (Basel, Switzerland) · 2024Article
- Omicron detection with large language models and YouTube audio data.medRxiv : the preprint server for health sciences · 2024Article
- TBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset.Science advances · 2024Article
- An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio.Bioengineering (Basel, Switzerland) · 2024Article
- Voice EHR: introducing multimodal audio data for health.Frontiers in digital health · 2024Article
- COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests.Scientific reports · 2023Article
- The smarty4covid dataset and knowledge base as a framework for interpretable physiological audio data analysis.Scientific data · 2023Article
- Human-centred artificial intelligence for mobile health sensing: challenges and opportunities.Royal Society open science · 2023Review
- Artificial Intelligence and Infectious Disease Imaging.The Journal of infectious diseases · 2023Article
- Differences in acoustic features of cough by pneumonia severity in patients with COVID-19: a cross-sectional study.ERJ open research · 2023Article
Corrections and comments
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To identify Coronavirus disease (COVID-19) cases efficiently, affordably, and at scale, recent work has shown how audio (including cough, breathing and voice) based approaches can be used for testing. However, there is a lack of exploration of how biases and methodological decisions impact these tools' performance in practice. In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. To investigate this, we collected a large crowdsourced respiratory audio dataset through a mobile app, alongside symptoms and COVID-19 test results. Within the collected dataset, we selected 5240 samples from 2478 English-speaking participants and split them into participant-independent sets for model development and validation. In addition to controlling the language, we also balanced demographics for model training to avoid potential acoustic bias. We used these audio samples to construct an audio-based COVID-19 prediction model. The unbiased model took features extracted from breathing, coughs and voice signals as predictors and yielded an AUC-ROC of 0.71 (95% CI: 0.65-0.77). We further explored several scenarios with different types of unbalanced data distributions to demonstrate how biases and participant splits affect the performance. With these different, but less appropriate, evaluation strategies, the performance could be overestimated, reaching an AUC up to 0.90 (95% CI: 0.85-0.95) in some circumstances. We found that an unrealistic experimental setting can result in misleading, sometimes over-optimistic, performance. Instead, we reported complete and reliable results on crowd-sourced data, which would allow medical professionals and policy makers to accurately assess the value of this technology and facilitate its deployment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.