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.

VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds / Pancaldi, F.; Pezzuto, G. S.; Cassone, G.; Morelli, M.; Manfredi, A.; D'Arienzo, M.; Vacchi, C.; Savorani, F.; Vinci, G.; Barsotti, F.; Mascia, M. T.; Salvarani, C.; Sebastiani, M.. - In: COMPUTERS IN BIOLOGY AND MEDICINE. - ISSN 0010-4825. - 142:(2022), pp. 105220-105220. [10.1016/j.compbiomed.2022.105220]

VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds

Sebastiani M.
2022-01-01

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.
2022
VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds / Pancaldi, F.; Pezzuto, G. S.; Cassone, G.; Morelli, M.; Manfredi, A.; D'Arienzo, M.; Vacchi, C.; Savorani, F.; Vinci, G.; Barsotti, F.; Mascia, M. T.; Salvarani, C.; Sebastiani, M.. - In: COMPUTERS IN BIOLOGY AND MEDICINE. - ISSN 0010-4825. - 142:(2022), pp. 105220-105220. [10.1016/j.compbiomed.2022.105220]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2977640
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