RT Journal Article
SR Electronic
T1 A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19
JF ERJ Open Research
JO erjor
FD European Respiratory Society
SP 00152-2022
DO 10.1183/23120541.00152-2022
VO 8
IS 4
A1 Guy Dori
A1 Noa Bachner-Hinenzon
A1 Nour Kasim
A1 Haitem Zaidani
A1 Sivan Haia Perl
A1 Shlomo Maayan
A1 Amin Shneifi
A1 Yousef Kian
A1 Tuvia Tiosano
A1 Doron Adler
A1 Yochai Adir
YR 2022
UL http://openres.ersjournals.com/content/8/4/00152-2022.abstract
AB 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.AI applied to full-spectrum auscultation (infrasound and audible) provides superior morbidity detection in COVID-19-related pneumonia compared with standard narrow-band auscultation restricted to the audible spectrum https://bit.ly/3oTpzEN