COVID-19 detection from audio: seven grains of salt

Coppock, Harry; Jones, Lyn; Kiskin, Ivan; Schuller, Björn W.

doi:10.1016/s2589-7500(21)00141-2

Cited by 44 publications

(29 citation statements)

References 7 publications

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“…Poor data quality leading to poorly trained AI/ML models that cannot perform in a clinical setting has been described extensively in the literature [1][2][3][4]. Inadequate sample sizes for training, validation, and testing are often secondary to the lack of availability of datasets that results in a lack of statistical significance and subsequent rejection by informed clinicians.…”

Section: Introductionmentioning

confidence: 99%

AI/ML Models to Aid in the Diagnosis of COVID-19 Illness from Forced Cough Vocalizations: Good Machine Learning Practice and Good Clinical Practices from Concept to Consumer for AI/ML Software Devices

Kelley¹,

Kelley²,

Kelley³

et al. 2021

Preprint

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From a comprehensive and systematic search of the relevant literature on signal data signature (SDS)-based artificial intelligence/machine learning (AI/ML) systems designed to aid in the diagnosis of COVID-19 illness, we identified the highest quality articles with statistically significant data sets for a head-to-head comparison to our own model in development. Further comparisons were made to the recently released “Good Machine Learning Practice (GMLP) for Medical Device Development: Guiding Principles” and, in conclusions, we proposed supplemental principles aimed at bringing AI/ML technologies in closer alignment GMLP and Good Clinical Practices (GCP).

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Section: Introductionmentioning

confidence: 99%

AI/ML Models to Aid in the Diagnosis of COVID-19 Illness from Forced Cough Vocalizations: Good Machine Learning Practice and Good Clinical Practices from Concept to Consumer for AI/ML Software Devices

Kelley¹,

Kelley²,

Kelley³

et al. 2021

Preprint

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“…We thank Humberto Perez-Espinosa and colleagues for their constructive points regarding our Comment, 1 which raised concerns over the work on COVID-19 detection from bioacoustic recordings. We take this opportunity to note that the study by Perez-Espinosa and colleagues 2 represented one of the superior COVID-19 audio datasets that were collected.…”

mentioning

confidence: 99%

“…We take this opportunity to note that the study by Perez-Espinosa and colleagues 2 represented one of the superior COVID-19 audio datasets that were collected. Although the study was not completely free from the “seven grains of salt” detailed in our Comment, 1 it was large scale, validated by quantitative RT-PCR, and the participants were blinded. We also applaud the recording of cycle threshold, which allowed for the comparison between model performance and viral load.…”

mentioning

confidence: 99%

Bias and privacy in AI's cough-based COVID-19 recognition – Authors' reply

Coppock¹,

Jones²,

Kiskin³

et al. 2021

The Lancet Digital Health

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“…We read with interest the Comment by Coppock and colleagues, 1 in which the authors express their thoughtful opinion about several simultaneous works by independent research groups worldwide (eg, Massachusetts Institute of Technology, National Research Council of Canada, University of Cambridge, and Swiss Federal Institute of Technology Lausanne). One of these works was our own; a pioneering, multicentre, international study 2 with a clinically validated dataset of forced coughs alongside quantitative RT-PCR from participants who physically attended a test centre.…”

mentioning

confidence: 99%

“…The Comment 1 focuses on human audio biometrics in general, albeit eight out of ten referenced works made use of coughs as their audio source. The use of cough sounds to detect respiratory system abnormalities had been investigated for at least 3 years before the COVID-19 pandemic.…”

mentioning

confidence: 99%