PulmoScan: A Practical Pulmonary Disease Pre-Screening System

Abstract

Automation of pulmonary disease identification has been a long-standing area of research and gained increased attention after the COVID-19 pandemic. However, existing respiratory sound classification algorithms exhibit significant limitations, including suboptimal performance, insufficient input robustness, and inadequate alignment with clinical evaluation metrics, thereby hindering their practical implementation. To address these limitations, we introduce PulmoScan, a practical pulmonary disease pre-screening system. PulmoScan comprises three fundamental modules: a Respiratory Sound Quality Validator that ensures the robustness of input data, a Runtime Decision Booster that improves performance and adapting to variating evaluation metrics, and a Symptom Enhancement Diagnoser that augments respiratory sound classification with comprehensive disease pre-screening capabilities. Beyond its primary function, PulmoScan exemplifies a methodological framework for translating theoretically limited algorithms into viable clinical applications, demonstrating essential considerations and procedural adaptations for real-world implementation.