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<p>Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are critical clinical events that necessitate prompt intervention, yet their detection remains challenging in primary care, especially in resource-limited regions where a lack of awareness leads to delayed diagnoses. To address this issue, we developed an AI-based AECOPD detection system that leverages standard smartphone microphones for auscultation, specifically designed for novice users and analyzing sounds to screen for AECOPD without requiring subjective patient-reported scales. Our system demonstrated robust performance, achieving an area under the curve (AUC) of 0.955 (95% CI: 0.929–0.976). A state-transition health-economic model projected per-capita net savings of 456.9 CNY (95% CI: −88.2 to 1,779.3) with a 90.3% probability of positive returns, supporting cost-effective implementation. This research highlights the potential of AI-driven solutions to enhance COPD management in underserved populations, providing a scalable tool to promote health equity where access to pulmonary specialists is constrained.</p>

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AI-driven smartphone screening for acute COPD exacerbations: enhancing health equity in developing regions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Yanbin Gong,
  • Chi Xu,
  • Caixia Mo,
  • Jianhui Wu,
  • Huiyong Lin,
  • Huaan Su,
  • Qingpeng Zhang,
  • Qian Zhang,
  • Shifang Yang

摘要

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are critical clinical events that necessitate prompt intervention, yet their detection remains challenging in primary care, especially in resource-limited regions where a lack of awareness leads to delayed diagnoses. To address this issue, we developed an AI-based AECOPD detection system that leverages standard smartphone microphones for auscultation, specifically designed for novice users and analyzing sounds to screen for AECOPD without requiring subjective patient-reported scales. Our system demonstrated robust performance, achieving an area under the curve (AUC) of 0.955 (95% CI: 0.929–0.976). A state-transition health-economic model projected per-capita net savings of 456.9 CNY (95% CI: −88.2 to 1,779.3) with a 90.3% probability of positive returns, supporting cost-effective implementation. This research highlights the potential of AI-driven solutions to enhance COPD management in underserved populations, providing a scalable tool to promote health equity where access to pulmonary specialists is constrained.