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

Abstract

The 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, using a standard stethoscope, sounds "normal". Chest CT is the gold standard for detecting COVID-19 pneumonia; however, radiation exposure, availability and cost preclude its utilization as a screening tool for COVID-19 pneumonia. In this study we hypothesized 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.Lung sounds signals were acquired, using a novel full spectrum (3–2,000Hz) stethoscope, from 164 patients with COVID-19 pneumonia, 61 non-COVID-19 pneumonia and 141 healthy subjects. A machine-learning classifier was constructed, and the data was classified into 3 groups: 1. Normal lung sounds 2. COVID-19 pneumonia 3. Non-COVID-19 pneumonia.Standard auscultation found that 72% of the non-COVID-19 pneumonia patients had abnormal lung sounds, compared to only 25% for the COVID-19 pneumonia patients. The classifier's sensitivity and specificity for the detection of COVID-19 pneumonia were 97% and 93%, respectively, when analyzing the sound and infrasound data, and they were reduced to 93% and 80% without the infrasound data (p<0.01 difference in ROC with and without infrasound).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.

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging

“…Various AI models have been used in detecting and analyzing lung sounds [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 ]. These methods have been tested in and proposed to be used in a multitude of clinical settings.…”

“…Limitations existed in the study due to background noises interrupting lung sound analysis with CNN models. However, this method can provide clinicians with useful early prognostic information to facilitate pre-treatment risk stratification and guide medical staff to conduct more intensive surveillance and treatment of patients at high risk of severe illness to reduce mortality [ 35 , 79 ].…”