Deep Learning-Based Quality Control and Diagnosis of Bronchial Images

Bronchoscopy is essential for diagnosing and treating lung diseases, yet conventional techniques are limited by incomplete anatomical coverage, unstable image quality, high rates of missed lesions, and significant operator dependency. These challenges exacerbate disparities in healthcare quality, especially in regions with unevenly distributed medical resources.

This study conducts a systematic analysis of the potential for adapting deep learning technologies to the field of medical endoscopy. It specifically explores the application prospects of artificial intelligence (AI) for enhancing the quality control and diagnostic analysis of bronchoscopic images.

The findings highlight AI's significant potential to innovate bronchoscopic image analysis. However, current research has limitations, particularly in the generalizability of models. Future work must focus on multicenter clinical validation to optimize model robustness and on developing real-time decision support systems to ultimately standardize bronchoscopic procedures and improve diagnostic efficiency.