The DOI matches its Crossref record; 10 of the 14 references it cites were found in a registry or an index.
COVID-19 detection from audio: seven grains of salt
Open accessAbstract
Digital mass testing for COVID-19 via a mobile phone application could be made possible through machine learning and its ability to identify patterns in data. COVID-19 appears to confer unique features in the audio produced by infected individuals,1 and machine learning COVID-19 detection from breath, cough, and speech audio recordings has yielded promising results.2–4 In this critique, we present seven major issues with this research and argue that further investigation is needed before conclusions about the detectability of COVID-19 from audio can be made.
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“While audio-based applications have seen significant advancements, deploying them in real-world settings, especially for audio-based COVID-19 detection, remains challenging [86].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [86]
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AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app
Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., & Nabeel, M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100378 · Cited by 621
COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings
Laguarta, J., Hueto, F., & Subirana, B. (2020) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2020.3026928 · Cited by 589
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
Brown, C., Chauhan, J., Grammenos, A., Han, J., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., & Mascolo, C. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3412865 · Cited by 447
The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms
Orlandic, L., Teijeiro, T., & Atienza, D. (2021) · Scientific Data · doi:10.1038/s41597-021-00937-4 · Cited by 346
Coppock, H., Gaskell, A., Tzirakis, P., Baird, A., Jones, L., & Schuller, B. (2021) · BMJ Innovations · doi:10.1136/bmjinnov-2021-000668 · Cited by 108
Self-citationAndreu-Pérez, J., Pérez-Espinosa, H., Timonet-Andreu, E. M., Kiani, M., Girón‐Pérez, M. I., Benítez-Trinidad, A. B., Jarchi, D., Rosales-Pérez, A., Gatzoulis, N., Reyes-Galaviz, O. F., et al. (2021) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3061402 · Cited by 105
A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems
Quatieri, T. F., Talkar, T., & Palmer, J. S. (2020) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2020.2998051 · Cited by 97
Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds
Bagad, P., Dalmia, A., Doshi, J., Nagrani, A., Bhamare, P., Mahale, A., Rane, S., Agarwal, N., & Panicker, R. (2020) · arXiv · doi:10.48550/arxiv.2009.08790 · Cited by 91
SARS-CoV-2 Detection From Voice
Pinkas, G., Karny, Y., Malachi, A., Barkai, G., Bachar, G. Y., & Aharonson, V. (2020) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2020.3026468 · Cited by 87
IATos: AI-powered pre-screening tool for COVID-19 from cough audio samplesPreprint
Pizzo, D. T., & Esteban, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.13247 · Cited by 24
Flagged first, then by citations
4 of the 14 references couldn’t be matched to a known paper.
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Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning
Chadaga, K., Chakraborty, C., Prabhu, S., Umakanth, S., Vivekananda Bhat, K., & Sampathila, N. (2022) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00499-4 · Cited by 58
Ponomarchuk, A., Burenko, I., Malkin, E., Nazarov, I., Kokh, V., Avetisian, M., & Евгеньевич, Ж. Л. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3142514 · Cited by 50
Gheisari, M., Ghaderzadeh, M., Li, H., Taami, T., Fernández‐Campusano, C., Sadeghsalehi, H., & Abbasi, A. A. (2023) · JMIR mhealth and uhealth · doi:10.2196/44406 · Cited by 39
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30
Blazing the trail for innovative tuberculosis diagnostics
Yerlikaya, S., Broger, T., Isaacs, C., Bell, D., Holtgrewe, L., Gupta‐Wright, A., Nahid, P., Cattamanchi, A., & Denkinger, C. M. (2023) · Infection · doi:10.1007/s15010-023-02135-3 · Cited by 27
Metadata
- Authors
- Coppock, H., Jones, L., Kiskin, I., & Schuller, B.
- Affiliations
- Imperial College London (GB)
- North Bristol NHS Trust (GB)
- University of Oxford (GB)
- Corresponding author
- Coppock, Harry
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 3
- Issue
- 9
- Pages
- e537-e538
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC8294804
- OpenAlex
- W3185939438
- Cited by
- 55
- References
- 14
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 7 · 2023: 15 · 2022: 15 · 2021: 8
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OPUS (Augsburg University) (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Keywords
- COVID-19 pandemic, audio-based diagnosis, speech analysis, cough, lung sounds, mobile application, large-scale testing, machine learning, diagnostic accuracy
- MeSH terms
- Machine Learning, COVID-19, SARS-CoV-2, COVID-19 Testing, Cough, Humans, Mass Screening, Respiration, Sound, Speech, Telemedicine, Cell Phone, Mobile Applications, Datasets as Topic
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