The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection.
Zhao Ren, Yi Chang, Katrin D Bartl-Pokorny and 2 others
PMID 35835648WHAT IT FOUND
An algorithm detecting COVID-19 from cough sounds beat chance but remained weak, especially for asymptomatic people.
It is not accurate enough for clinical screening. Self-reported symptoms and unverified test status make the data unreliable for deciding patient care.
Key findings
01The best model distinguishing COVID-19 positive from negative individuals achieved an unweighted average recall of 0.632, which is significantly better than chance but far from diagnostic accuracy.
02Performance was lowest for asymptomatic individuals, where the model incorrectly assigned the highest ratio of positive samples to the negative class.
03Participants self-reported their COVID-19 status without providing test confirmation, and coughs were recorded at unspecified times during infection.
STILL TO COME
How it was doneWhat they foundWhat it means for SLPs
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What it does not show
COVID-19 status was self-reported without test verification. The time between infection or testing and cough recording was not controlled, meaning disease stage varied. Symptom severity was not recorded, only presence. Crowd-sourced audio quality varied, and participants were advised to use plastic bags, which degrades acoustic fidelity. The dataset was heavily imbalanced, with far more negative than positive samples. It is possible that multiple recordings came from the same person or household, inflating sample independence.
Declared interests
The authors declared no conflicts of interest.
The easy way to misread this
Do not interpret the finding that the algorithm beat chance as evidence that cough sounds are a viable clinical diagnostic marker. The accuracy is too low for screening, and the self-reported, unverified nature of the data means the algorithm may be detecting recording habits or symptom severity rather than the virus itself.