用手机听诊+新算法，提升肺部声音诊断准确率
Patient Domain Supervised Contrastive Learning for Lung Sound Classification Using Mobile Phone
- 基于患者领域监督对比学习，解决手机与电子听诊器音质差异
- 相比原模型提升2.4%准确率，有效应对患者间差异
- 适合远程医疗、基层筛查，推动肺病诊断普惠化
听诊对肺部疾病诊断至关重要。新冠疫情期间暴露出传统面对面听诊的局限性。为克服这一问题，数字听诊器与人工智能的进步催生了新型诊断方法。本研究利用智能手机麦克风录制并分析肺部声音，面临两大挑战：电子听诊器与手机麦克风间的音频风格差异，以及患者个体间变异。为此，我们提出患者领域监督对比学习（PD-SCL）方法，结合音频频谱变换器（AST）模型，使性能相比原始模型提升2.4%。结果表明，智能手机可有效实现肺部声音诊断，缓解患者数据不一致问题，展现出在非临床场景中的广泛应用潜力。本研究为后疫情时代肺部疾病检测的可及性提供支持。
原文摘要 · Abstract (English)
Auscultation is crucial for diagnosing lung diseases. The COVID-19 pandemic has revealed the limitations of traditional, in-person lung sound assessments. To overcome these issues, advancements in digital stethoscopes and artificial intelligence (AI) have led to the development of new diagnostic methods. In this context, our study aims to use smartphone microphones to record and analyze lung sounds. We faced two major challenges: the difference in audio style between electronic stethoscopes and smartphone microphones, and the variability among patients. To address these challenges, we developed a method called Patient Domain Supervised Contrastive Learning (PD-SCL). By integrating this method with the Audio Spectrogram Transformer (AST) model, we significantly improved its performance by 2.4\% compared to the original AST model. This progress demonstrates that smartphones can effectively diagnose lung sounds, addressing inconsistencies in patient data and showing potential for broad use beyond traditional clinical settings. Our research contributes to making lung disease detection more accessible in the post-COVID-19 world.
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