Abstract
BackgroundLung 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.MethodsA total of 1000 consecutive, patients aged ≥ 16 years and meeting COVID-19 testing criteria will be recruited at screening sites and amongst inpatients of the internal medicine department at the Geneva University Hospitals, starting from October 2020. COVID-19 is diagnosed by RT-PCR on a nasopharyngeal swab and COVID-positive patients are followed up until outcome (i.e., discharge, hospitalisation, intubation and/or death). At inclusion, demographic and clinical data are collected, such as age, sex, medical history, and signs and symptoms of the current episode. Additionally, lung auscultation will be recorded with a digital stethoscope at 6 thoracic sites in each patient. A deep learning algorithm (DeepBreath) using a Convolutional Neural Network (CNN) and Support Vector Machine classifier will be trained on these audio recordings to derive an automated prediction of diagnostic (COVID positive vs negative) and risk stratification categories (mild to severe). The performance of this model will be compared to a human prediction baseline on a random subset of lung sounds, where blinded physicians are asked to classify the audios into the same categories.DiscussionThis approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at various levels of healthcare, especially in the context of decentralised triage and monitoring.Trial registration: PB_2016-00500, SwissEthics. Registered on 6 April 2020.
Highlights
Lung auscultation is fundamental to the clinical diagnosis of respiratory disease
Glangetas et al BMC Pulm Med (2021) 21:103. This approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at vari‐ ous levels of healthcare, especially in the context of decentralised triage and monitoring
Assuming a similar discriminative power compared to a previous work distinguishing healthy and pathological lung sounds in pneumonia from 80 patients in balanced classes (40 pathological and 40 control) with 8 auscultation sites of 30 s each, we estimate to achieve convergence at above 80% area under the curve (AUC)-Receiver Operating Characteristic (ROC) with 10% variability using the same number of patients in each class
Summary
Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a promising strategy for diagnosing and monitoring infectious diseases such as Coronavi‐ rus-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 autono‐ mous stethoscope benchmarked against human expert interpretation. Follow-up comparative assessments are based on the user’s ability to remember these patterns from a previous time point, or mentally reconstruct them from descriptions written in clinical notes As this interpretation is a highly subjective skill, inter-listener variability limits interoperability, where accuracy ranges widely with experience and differs across specialities [2, 3]. Other sources of heterogeneity may originate from differences in the intrinsic properties of the stethoscope and extrinsic patient-related factors such as obesity, ambient noise and patient compliance (e.g., crying child)
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