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Deep Learning-Based Quality Control and Diagnosis of Bronchial Images.
Yong Zhou1, Felix J F Herth2, Bin Liu3
1Endoscopy Clinic Center, Xi'an Chest Hospital, Xi'an, China.
Respiration; International Review of Thoracic Diseases
|November 27, 2025
Summary
Artificial intelligence (AI) shows promise for improving bronchoscopy by enhancing image analysis and quality control in lung disease diagnosis. Further research is needed for real-world application and to standardize procedures.
Area of Science:
- Medical imaging
- Artificial intelligence in medicine
- Pulmonology
Background:
- Conventional bronchoscopy faces limitations including incomplete anatomical visualization, variable image quality, missed lesions, and operator dependence.
- These limitations contribute to healthcare disparities, particularly in resource-limited settings.
Purpose of the Study:
- To systematically analyze the potential of deep learning technologies for medical endoscopy.
- To explore the application of artificial intelligence (AI) for quality control and diagnostic analysis in bronchoscopic imaging.
Main Methods:
- Systematic analysis of deep learning adaptation in medical endoscopy.
- Exploration of AI applications for bronchoscopic image quality control.
- Investigation of AI for diagnostic analysis of bronchoscopic images.
Main Results:
- AI demonstrates significant potential to revolutionize bronchoscopic image analysis.
- Current AI models for bronchoscopy show limitations in generalizability.
Conclusions:
- AI can innovate bronchoscopic image analysis and diagnostic efficiency.
- Future research requires multicenter clinical validation to enhance model robustness.
- Development of real-time AI decision support systems is crucial for standardizing procedures.
