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

Since the invention of the stethoscope by René-Théophile-Hyacinthe Laennec in the nineteenth century, the interpretation of internal body sounds has made the stethoscope a ubiquitous part of a doctor’s uniform. As stethoscopes are easy to handle, inexpensive and non-invasive, they can provide valuable clinical information at any level of care, and are a fundamental step in the preliminary clinical diagnosis and early assessment of pulmonary disease to shorten delays in diagnosis and emergency management [1]. However, the utility of this tool is ultimately dependent on the user’s perceptual capacity to discriminate and interpret pathological patterns in sound across the respiratory cycle. Follow-up comparative assessments are then 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]. Discrepancies also arise due to a lack of standardisation in nomenclature [3] rendering the results equivocal and/or incomprehensible to other caregivers. 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).

To better standardise the detection of abnormal lung sounds, there has been a recent interest in digitizing respiratory sound acquisition using electronic stethoscopes [4‐7] and then analysing it using artificial intelligence (AI) methods of deep learning [8‐13]. For the COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), AI methods have revealed clear patterns in its radiological presentation [14‐19], and some preliminary evidence on the predictive capacity of respiratory sound is emerging. For instance, the application of simple models such as logistic regression and support vector machine (SVM) were able to predict the diagnosis of COVID-19 from breath and cough sounds crudely collected on a mobile application with an area under the curve (AUC) of around 70% [20]. Another group achieved above 95% sensitivity and specificity on discriminating COVID-19 coughs from other pathologies as well as healthy patients [21]. However, no evidence exists for the potential of digital lung sounds for early detection and, more importantly, risk stratification in COVID-19. Indeed, while around 80% of infections are either asymptomatic or self-resolving after a few weeks of mild disease, the remaining 20% can rapidly progress to acute respiratory distress syndrome with a poor prognosis and high mortality [22, 23]. Hence, early risk stratification is crucial for prompt referral and early intervention, as well as the appropriate allocation of limited hospital resources.

Owing to the initial non-specific symptomatic presentation of COVID-19, diagnosis and risk stratification are based on more objective paraclinical exams, such as reverse transcription polymerase chain reaction (RT-PCR) to detect the genetic signatures of SARS-CoV-2 in nasal-pharyngeal swabs for diagnosis, and high-resolution chest computerized tomography (CT) to estimate prognosis [24, 25]. However, even RT-PCR may yield false-negative results [26] and in times of high transmission, testing backlogs can render turn-around times clinically irrelevant [27]. On the other hand, risk stratification by CT is inconvenient for a variety of reasons. Firstly, these machines are usually housed in centralised high-level healthcare infrastructures, inappropriate for triage, and necessitate the transfer/handling of potentially infectious patients. Secondly, they expose patients to ionizing radiation and, thirdly, the cost and skill required to acquire and use these machines have made CT scanners rarely available in many parts of the world.

Thus, we aim to develop a set of early diagnostic and risk-stratification algorithms for COVID-19 from lung auscultations. To this end, we will collect standardised digital lung recordings from patients triaged for COVID-19 testing at screening sites or already hospitalised for diagnosed COVID-19. We hypothesize that early diagnostic and prognostic acoustic signatures of COVID-19 can be detected by deep learning independently of the caregiver’s auscultation skills, thus better standardising decision making and resource allocation.

The recent advances in deep learning are promising to support physicians in standardizing the detection and interpretation of complex patterns in pulmonary diseases. Artificial intelligence has proven to outperform physicians in discriminating respiratory pathologies via respiratory functional explorations [35], symptoms [36, 37], and/or radiological examinations [38]. The development of AI algorithms for the analysis of respiratory acoustic signals has been proposed previously [9, 10]. It remains to be determined whether an AI algorithm can be used as an initial and accurate screening tool for patients suspected of COVID-19. Such an algorithm has the potential to support early diagnosis, to guide the allocation of resources and identify those in need of early hospitalisation [39].

This study aims to collect a standardised dataset of digital lung auscultations and derive a deep leaning model able to detect the acoustic signatures of the presence and severity of COVID-19. We hypothesize that automated interpretation of lung auscultation could better democratize the accuracy of this critical clinical exam beyond the individual capabilities of the health workers that a patient may be fortunate (or unfortunate) to have. We plan to incorporate this algorithm into an autonomous digital stethoscope (currently under development), that could help decentralise high quality respiratory examination and monitoring, and perhaps even empower patients to assess themselves, which would reduce nosocomial infections occurring during the proximity of a traditional clinical exam. Making high quality lung auscultation accessible to unskilled/decentralized actors could broaden COVID-19 mass screening and identify patients at earlier stages of disease, which would prevent transmission as well as allow earlier pharmacological interventions and nuance the pre-test probability to better select candidates for further PCR testing (i.e. resource conservation). Additionally, such a tool could empower patients confined at home to self-monitor their symptoms to inform telemedicine and personalize care.

This study has several limitations. First, ICU-inpatients with severe COVID-19 presentations requiring ventilatory supports will not be considered. This will limit longitudinal auscultations on critically-ill patients and prevent risk stratification assessment of those who will progress very unfavourably (or favourably) from this stage onwards. Second, since this study is a single centre study conducted in a high-income country with easy access to health care, caution will be taken when assessing the generalisability of these results to different populations, especially in resource-limited countries and remote areas. Finally, our study population will be recruited primarily from an emergency department triage centre and COVID-19-dedicated hospitalization units, which may suggest more acute symptoms and pathological lung sounds than those encountered in ambulatory care services.