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

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", 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.

A comparison of the power of breathing sounds signals acquired with a smart stethoscope from a cohort of COVID-19 patients at peak disease, and pre-discharge from the hospital.

Objectives: To characterize the frequencies of breathing sounds signals (BS) in COVID-19 patients at peak disease and pre-discharge from hospitalization using a Smart stethoscope.

Methods: Prospective cohort study conducted during the first COVID-19 wave (April-August 2020) in Israel. COVID-19 patients (n = 19) were validated by SARS-Cov-2 PCR test.