Bronchoscopy is a valuable tool for the diagnosis and treatment of lung diseases. However, traditional bronchoscopy techniques face challenges such as incomplete examination coverage, unstable image quality, high lesion leakage rate, and high operator dependency, which are particularly prominent in the context of uneven distribution of healthcare resources. In recent years, the rapid development of deep learning technology has provided a new path for the innovation of bronchial image quality control and diagnosis. This study systematically analyses the technology migration potential of AI in the field of medical endoscopy and explores its application prospects in bronchoscopic image analysis. At the same time, this paper also summarizes the shortcomings of bronchial image analysis in the current study, and in the future, it is necessary to further optimize the model generalization ability through multi-center clinical validation and explore the clinical application of real-time decision support system, so as to comprehensively improve the standardization and diagnostic efficiency of bronchial images.
Zhou et al. (Thu,) studied this question.