VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds. (March 2022)

• Record Type: Journal Article
• Authors: Pancaldi, Fabrizio; Pezzuto, Giuseppe Stefano; Cassone, Giulia; Morelli, Marianna; Manfredi, Andreina; D'Arienzo, Matteo; Vacchi, Caterina; Savorani, Fulvio; Vinci, Giovanni; Barsotti, Francesco; Mascia, Maria Teresa; Salvarani, Carlo; Sebastiani, Marco
• Is Part Of: Computers in biology and medicine. Volume 142(2022)
• Publication Date: 2022-03
• Subjects: SARS-CoV-2 -- COVID-19 -- Interstitial pneumonia -- Electronic stethoscope -- Lung sounds -- Audio processing -- VECTOR; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105220
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The coronavirus disease 2019 (COVID-19) has severely stressed the sanitary systems of all countries in the world. One of the main issues that physicians are called to tackle is represented by the monitoring of pauci-symptomatic COVID-19 patients at home and, generally speaking, everyone the access to the hospital might or should be severely reduced. Indeed, the early detection of interstitial pneumonia is particularly relevant for the survival of these patients. Recent studies on rheumatoid arthritis and interstitial lung diseases have shown that pathological pulmonary sounds can be automatically detected by suitably developed algorithms. The scope of this preliminary work consists of proving that the pathological lung sounds evidenced in patients affected by COVID-19 pneumonia can be automatically detected as well by the same class of algorithms. In particular the software VECTOR, suitably devised for interstitial lung diseases, has been employed to process the lung sounds of 28 patient recorded in the emergency room at the university hospital of Modena (Italy) during December 2020. The performance of VECTOR has been compared with diagnostic techniques based on imaging, namely lung ultrasound, chest X-ray and high resolution computed tomography, which have been assumed as ground truth. The results have evidenced a surprising overall diagnostic accuracy of 75% even if the staff of the emergency room has not been suitably trained for lung auscultation and the parameters of the software have not been optimized to detect interstitial pneumonia. These results pave the way to a new approach for monitoring the pulmonary implication in pauci-symptomatic COVID-19 patients. Graphical abstract: Image 1 Highlights: Early detection of interstitial pneumonia is fundamental for the survival of COVID-19 patients Lung sounds represent a reliable marker of COVID-19 pneumonia Our software called VECTOR can detect COVID-19 pulmonary implications from lung sounds VECTOR showed a positive predictive value of 85.7%, negative predictive value of 64.3%, sensitivity and specificity were 70.6% and 81.8%, respectively. VECTOR enables a new approach to the management of COVID-19 patients