Dataset Information

A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19.

ABSTRACT:

Background

The coronavirus disease 2019 (COVID-19) outbreak has rapidly spread around the world, causing a global public health and economic crisis. A critical limitation in detecting COVID-19-related pneumonia is that it is often manifested as a "silent pneumonia", i.e. pulmonary auscultation that sounds "normal" using a standard stethoscope. Chest computed tomography is the gold standard for detecting COVID-19 pneumonia; however, radiation exposure, availability and cost preclude its utilisation as a screening tool for COVID-19 pneumonia. In this study we hypothesised that COVID-19 pneumonia, "silent" to the human ear using a standard stethoscope, is detectable using a full-spectrum auscultation device that contains a machine-learning analysis.

Methods

Lung sound signals were acquired, using a novel full-spectrum (3-2000 Hz) stethoscope, from 164 COVID-19 pneumonia patients, 61 non-COVID-19 pneumonia patients and 141 healthy subjects. A machine-learning classifier was constructed and the data were classified into three groups: 1) normal lung sounds, 2) COVID-19 pneumonia and 3) non-COVID-19 pneumonia.

Results

Standard auscultation found that 72% of the non-COVID-19 pneumonia patients had abnormal lung sounds compared with only 25% of the COVID-19 pneumonia patients. The classifier's sensitivity and specificity for the detection of COVID-19 pneumonia were 97% and 93%, respectively, when analysing the sound and infrasound data, and they were reduced to 93% and 80%, respectively, without the infrasound data (p<0.01 difference in receiver operating characteristic curves with and without infrasound).

Conclusions

This study reveals that useful clinical information exists in the infrasound spectrum of COVID-19-related pneumonia and machine-learning analysis applied to the full spectrum of lung sounds is useful in its detection.

SUBMITTER: Dori G

PROVIDER: S-EPMC9501643 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19.

Dori Guy G Bachner-Hinenzon Noa N Kasim Nour N Zaidani Haitem H Perl Sivan Haia SH Maayan Shlomo S Shneifi Amin A Kian Yousef Y Tiosano Tuvia T Adler Doron D Adir Yochai Y

ERJ open research 20221024 4

<h4>Background</h4>The coronavirus disease 2019 (COVID-19) outbreak has rapidly spread around the world, causing a global public health and economic crisis. A critical limitation in detecting COVID-19-related pneumonia is that it is often manifested as a "silent pneumonia", <i>i.e.</i> pulmonary auscultation that sounds "normal" using a standard stethoscope. Chest computed tomography is the gold standard for detecting COVID-19 pneumonia; however, radiation exposure, availability and cost preclud ...[more]

PMID: 36284830

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Dataset Information

A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19.

Background

Methods

Results

Conclusions

Publications

A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19.

Similar Datasets

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