Sound-based diagnostic test for Covid shows 93% accuracy: IISc scientists 

The scientists at the Indian Institute of Science, Bengaluru, said they are in the process to submit the results of research to the ICMR for final approval.

Published: 02nd June 2021 06:29 PM  |   Last Updated: 02nd June 2021 06:29 PM   |  A+A-

The Indian Institute of Science, Bengaluru

Express News Service

NEW DELHI: Can Covid be detected using acoustics and symptoms? Yes and with a nearly 93% accuracy, claimed scientists at the Indian Institute of Science, Bengaluru. The team is in the process to submit the results of research to the ICMR for final approval.
Project Coswara was launched last year as an attempt to build a diagnostic tool for Covid diagnosis based on respiratory, cough, and speech sounds. The research direction of identifying acoustic bio-markers of respiratory diseases has received renewed interest following the onset of Covid pandemic. 

The team conducted a study to understand how respiratory sounds of healthy individuals are different from that of Covid patients and collected sound and symptom samples from 1,699 participants (157 Covid positive) between the age group 15-80 years. 

“In this paper, we design an approach to Covid diagnostic using crowd-sourced multi-modal data. The data resource, consisting of acoustic signals such as cough, breathing, and speech signals, along with the data of symptoms, are recorded using a web-application over a period of ten months,” said authors, as the study is under review in the IEEE Journal of Biomedical and Health Informatics. 

Sriram Ganapathy, Assistant Professor at IISc, said that the sound-based diagnostic test for Covid shows 93% accuracy and they are ready to approach the ICMR for final approval for commercial use. 

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“We would like to point out that the majority of the data came from participants who had just been discharged from the hospital facility. This indicates that the acoustic bio-markers of Covid may last for longer periods of time. We foresee that the use of simple classifiers and models would allow the diagnostic methods to be more interpretable. The proposed methodology combines all the advantages of being a rapid, low-cost, scalable, and remotely usable testing approach,” noted authors. 

The data used in this study comes from a web based crowd-sourced data collection platform. Each participant contributes 9 audio recordings -- shallow and deep breathing, shallow and heavy cough, sustained phonation of three vowels and fast and normal pace 1 − 20 number counting. Alongside this, each participant also records current health status (Covid infection, symptoms and co-morbidity, if any), gender, age, and broad geographical location.
No personally identifiable information is collected. The dataset collection protocol is approved by the Human Ethics Committee of the Indian Institute of Science, Bangalore and the P.D. Hinduja National Hospital and Medical Research Center, Mumbai, India. 

The performance (with 69 per cent sensitivity at 95% specificity) obtained on the test set surpasses the benchmark set by the Indian council of medical research (ICMR) for approval of point-of-care tests (POCTs) with (≥ 50% sensitivity at ≥ 95% specificity). The WHO blueprint on Covid diagnostic tests highlights the urgent need for developing POCTs, patient testing done at or near the location of the patient.


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