Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.

Journal: BMC pulmonary medicine
PMID: 33761909

Abstract

BACKGROUND: Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a particularly promising strategy for diagnosing and monitoring infectious diseases such as Coronavirus-19 disease (COVID-19) where automated analyses could help decentralise care and better inform decision-making in telemedicine. This protocol describes the standardised collection of lung auscultations in COVID-19 triage sites and a deep learning approach to diagnostic and prognostic modelling for future incorporation into an intelligent autonomous stethoscope benchmarked against human expert interpretation.

Authors

  • Alban Glangetas
    Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1205, Geneva, Switzerland.
  • Mary-Anne Hartley
    Intelligent Global Health, Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
  • Aymeric Cantais
    Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1205, Geneva, Switzerland.
  • Delphine S Courvoisier
    Faculty of Medicine, University of Geneva, Geneva, Switzerland.
  • David Rivollet
    Essential Tech Centre, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
  • Deeksha M Shama
    Intelligent Global Health, Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
  • Alexandre Perez
    Geneva University Hospitals, Geneva, Switzerland.
  • Hervé Spechbach
    Division of Primary Care Medicine, Department of Community Medicine, Geneva University Hospitals, Geneva, Switzerland.
  • Véronique Trombert
    Department of Internal Medicine and Rehabilitation, Geneva University Hospitals, Geneva, Switzerland.
  • Stéphane Bourquin
    Department of Micro-Engineering, Geneva School of Engineering, Architecture and Landscape (HEPIA), Geneva, Switzerland.
  • Martin Jaggi
    Intelligent Global Health, Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
  • Constance Barazzone-Argiroffo
    Paediatric Pulmonology Unit, Department of Women, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland.
  • Alain Gervaix
    Department of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland.
  • Johan N Siebert
    Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1205, Geneva, Switzerland. Johan.Siebert@hcuge.ch.

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