The use of chest auscultations to ?listen? to and diagnose lung infections has been in practice since the invention of the stethoscope in the early 1800s. While it is a versatile tool that is universally used to complement clinical observation and other diagnosis methods (e.g. chest palpation, X-rays), it remains an outdated technology that has not evolved much beyond its early design. Its use is limited by subjectivity and inconsistency in interpreting chest sounds, inter-listener variability, need for advanced medical expertise as well as vulnerability to ambient noise that masks the presence of sound patterns of interest. In the current project, we propose to design a novel smart stethoscope to automate diagnosis of chest auscultations; especially for pediatric use. Over 2 million children die every year of acute lower respiratory infections (ALRI), the leading cause of childhood mortality worldwide. Our hypothesis is that if lung sounds are robustly acquired and analyzed, they are sufficiently informative to result in quantifiable improvements in detection accuracy of lung pathologies. By improving diagnosis capability using a low-cost technology, the proposed smart stethoscope will enhance resource and case management of ALRI, especially in impoverished settings that lack alternative diagnosis tools such as X-rays. This proposal focuses on two key components for improving efficacy of lung auscultation diagnosis:
Aim 1 : Designing the smart stethoscope technology. This effort takes a different engineering direction than devices currently on the market by employing novel transducer and microphone arrays in a layout that mitigates issues with ambient noise and signal stability. The expected outcome is to provide medical practitioners with a low-cost device that offers noise-control, signal amplification and stable recordings. We will test this technology at the Johns Hopkins Pediatric Emergency Hospital.
Aim 2 : Augmenting the smart stethoscope with computer-aided diagnosis. We propose adaptive signal processing methods for analyzing lung signals to enable differentiating normal from pathological cases. The expected outcome is to improve the specificity and sensitivity of lung diagnosis using computer-aided analyses, and help inform clinical decisions and ALRI case management. The efficacy of the algorithm is directly evaluated in a study at a children's hospital in Peru, using radiographic pneumonia as benchmark. The site is chosen as representative of applicability of the proposed device in a low-resource setting. The proposal is a multidisciplinary effort that draws upon the expertise of engineers and medical experts, with close interaction and ongoing validation in patient populations. Its overarching goal is to improve sensitivity and specificity of pulmonary diagnosis using auscultations. The overall outcome of this effort is a point-of-care technology that is effective, low-cost, and deployable for pulmonary-health monitoring in hospitals, clinics, low resource community centers, and potentially home-based monitoring.

Public Health Relevance

Stethoscopes are valuable tools for clinical diagnosis of respiratory infections; though, they remain limited by a number of issues including subjectivity, inter-listener variability, need for advanced medical expertise as well as vulnerability to ambient noise that can mask lung sounds of interest. The current proposal develops a smart stethoscope using active acoustics and computerized lung sound analysis to inform clinical decisions about Acute Lower Respiratory Infections (ALRI), the leading cause of childhood mortality worldwide. The proposed system offers an innovative solution to develop a point-of-care mobile system that improves resource and case management of ALRI and increases the specificity and sensitivity of diagnosis of pneumonia cases; hence complementing and potentially replacing alternative diagnosis tools that are not widely available, especially in resource-poor areas.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
Project #
Application #
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Blaisdell, Carol J
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Johns Hopkins University
Engineering (All Types)
Schools of Engineering
United States
Zip Code
Emmanouilidou, Dimitra; McCollum, Eric D; Park, Daniel E et al. (2018) Computerized Lung Sound Screening for Pediatric Auscultation in Noisy Field Environments. IEEE Trans Biomed Eng 65:1564-1574
Huang, Nicholas; Slaney, Malcolm; Elhilali, Mounya (2018) Connecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory Signals. Front Neurosci 12:532
Bellur, Ashwin; Elhilali, Mounya (2017) Feedback-Driven Sensory Mapping Adaptation for Robust Speech Activity Detection. IEEE/ACM Trans Audio Speech Lang Process 25:481-492
Snyder, Joel S; Elhilali, Mounya (2017) Recent advances in exploring the neural underpinnings of auditory scene perception. Ann N Y Acad Sci 1396:39-55
Kaya, Emine Merve; Elhilali, Mounya (2017) Modelling auditory attention. Philos Trans R Soc Lond B Biol Sci 372: