Deep Learning Evaluates COPD Diagnosis And Grading
A systematic review and meta-analysis published in J Med Internet Res (2026) evaluated deep learning models for diagnosing and grading COPD, searching literature through November 1, 2025 and including 56 studies with 886,753 participants. Pooled binary detection performance was sensitivity 0.87, specificity 0.88, AUC 0.93 (CT models AUC 0.92; respiratory-sound models AUC 0.98), whereas multiclass GOLD staging accuracy was inconsistent, and substantial heterogeneity and limited external validation warrant caution.
Scoring Rationale
Comprehensive meta-analysis across 56 studies shows high binary detection accuracy; limited by substantial heterogeneity and scarce external validation.
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