SCH: Smart Auscultation for Pulmonary Diagnostics and Imaging

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The stethoscope is a ubiquitous technology used to listen to sounds from the chest in order to assess lung or heart conditions. Despite its universal use, it is considered an unreliable diagnosis tool due to a number of limitations: masking by noise, need for highly trained users and ear to interpret lung sounds and subjectivity in interpreting auscultation sounds. Still, one of the reasons auscultations are a staple of clinical screening is that sound is one the cheapest, fastest and most readily available biomarkers. The simple fact of breathing involves sound traveling through chest cavities that will be affected by presence of obstructions or abnormalities. While the signature of these air flow disruptions may be concealed, the right engineering innovation should not only identify their presence but can be extended as an imaging modality to identify their location, which would be a novel use of breath sounds to image lung cavities. The proposed smart auscultation technology is innovative in three ways: (i) it develops a machine learning architecture that imposes finite-element airway propagation constraints and stochastic variational inference using recurrent neural networks, (ii) a novel piezo-sensing material with tunable acoustic impedance that matches the skin hence eliminating air as transmission medium between the chest and device diaphragm which virtually eliminates pick up of any ambient noise, (iii) an array device that leverages the piezo-sensor to develop an imaging device using passive breathing sounds (instead of radiations or ultrasound probes). The proposed technology is extremely low-cost, deployable under adverse conditions, usable for immediate clinical examination as well as extendable for monitoring as a wearable device. The new technology will be field tested directly in case/control studies at the Johns Hopkins pediatric ER and pulmonary clinics to validate localization accuracy from the auscultation array using physicians’ judgments as gold standard. If successful, this technology will complement alternative, often costly and time-consuming diagnosis schemes (X-rays or ultrasounds which often cost $100-$1000’s) to offer a fast, cheap (few $) and accessible tool that can be widely disseminated from community clinics to hospitals and potentially home-based health monitoring. Given the dire public health need in addressing ALRI challenges, the proposed low-cost and efficient technology can be a game changer as a point-of-care aid to triage cases that require further medical attention.