A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19. Issue 4 (24th October 2022)

• Record Type: Journal Article
• Authors: Dori, Guy; Bachner-Hinenzon, Noa; Kasim, Nour; Zaidani, Haitem; Perl, Sivan Haia; Maayan, Shlomo; Shneifi, Amin; Kian, Yousef; Tiosano, Tuvia; Adler, Doron; Adir, Yochai
• Is Part Of: ERJ open research. Volume 8:Issue 4(2022)
• Publication Date: 2022-10-24
• Subjects: Respiratory organs -- Diseases -- Periodicals; Respiration -- Periodicals; Respiration; Respiratory organs -- Diseases; Respiratory organs -- Diseases -- Treatment; Respiratory Tract Diseases; Electronic journals; Fulltext; Internet Resources; Periodicals; Periodical; 616.2005
• DOI: 10.1183/23120541.00152-2022
• Languages: English
• ISSNs: 2312-0541

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. 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