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Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.