SputumLocator: Enhancing Airway Clearance with Auscultation-based Sputum Localization

Airway clearance is essential for managing Muco-Obstructive Lung Diseases (MOLDs). Percussion, a widely used airway clearance technique (ACT) in community and home care settings, is favored for its ease of implementation compared to other complex techniques. However, percussion is time-consuming and physically demanding for both caregivers and patients, as caregivers typically perform percussion on the entire back to avoid missing accumulated sputum when its exact location is unknown. Therefore, accurate sputum localization can significantly enhance the percussion experience. Current clinical methods for sputum localization typically rely on imaging techniques, which are costly, expose patients to radiation, and are usually performed only once during diagnosis, thereby limiting their application to inpatient settings. Alternatively, some medical professionals combine auscultation with other clinical assessments, but this approach requires substantial clinical experience and is impractical for community or home care settings where medical experts are unavailable. To address these limitations, we introduce SputumLocator, an innovative sputum localization system based on digital stethoscopes. SputumLocator leverages standard auscultation procedures to detect accumulated sputum in the four quadrants of the back, which is straightforward and highly practical. SputumLocator comprises two components: SputumEmbedder, which extracts key abnormal sounds and their spatial features using a Transformer-based feature extractor, and SputumClassifier, which maps these features to determine sputum presence in each region via a Convolutional Block Attention Module (CBAM). Given the limited availability of annotated sputum data, we developed a pretraining method based on Embedding on Masked Data (EOM) and enhanced model robustness through a Teacher-Student Architecture (TSA) that integrates noisy data. In collaboration with a medical institution, we evaluate SputumLocator on 43 patients with diverse physiological characteristics and under varying recording conditions. Experimental results demonstrate that SputumLocator achieves high accuracy with an overall sensitivity of 0.97, specificity of 0.82, and F1-Score of 0.83, maintaining robustness across different thoracic regions, genders, and disease types.

CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Applied computing → Health informatics.