Respiration; international review of thoracic diseases · 2026
Artificial Intelligence in Interventional Pulmonology.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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You Zhen-Ding, He Wei, Liu Jing, Tang Jiaxin, Li Hongjia, Li Zhaohui, Bai Yanfei, Li Shiyue, Zhong Changhao
Аннотация
BACKGROUND: Artificial intelligence (AI) has revolutionized interventional pulmonology (IP) by enhancing diagnostic accuracy, procedural efficiency, and training standardization. This review synthesizes current advancements and applications of AI across four key domains: education, imaging, navigation, and robotic bronchoscopy systems (RBS).
SUMMARY: In education, AI-powered convolutional neural networks (CNNs) improve airway structure recognition with an accuracy of 94.7%, thereby reducing learning curves and minimizing diagnostic errors. For imaging, deep learning models achieve lesion identification accuracy comparable to that of senior physicians (area under the curve: 0.940-0.981), enabling real-time intra-procedural guidance. Navigation technologies, such as virtual bronchoscopic navigation (VBN) and electromagnetic navigation bronchoscopy (ENB), enhance accessibility to peripheral lesions, achieving diagnostic yields of 77.9%-94.4% with excellent safety. RBS integrate AI-driven navigation and real-time imaging, achieving biopsy success rates up to 98.8% and diagnostic yield over 90%, while minimizing pneumothorax risks. Despite these advancements, challenges remain in algorithm validation, data privacy, and ethical considerations. AI demonstrates transformative potential in standardizing bronchoscopy practices, expanding access to underserved regions, and advancing personalized treatments.
KEY MESSAGE: AI significantly enhances the diagnostic accuracy and procedural safety of IP across education, imaging, navigation, and robotic systems; despite ongoing challenges in algorithm validation, AI is propelling the field toward standardized practices and personalized medicine.
SUMMARY: In education, AI-powered convolutional neural networks (CNNs) improve airway structure recognition with an accuracy of 94.7%, thereby reducing learning curves and minimizing diagnostic errors. For imaging, deep learning models achieve lesion identification accuracy comparable to that of senior physicians (area under the curve: 0.940-0.981), enabling real-time intra-procedural guidance. Navigation technologies, such as virtual bronchoscopic navigation (VBN) and electromagnetic navigation bronchoscopy (ENB), enhance accessibility to peripheral lesions, achieving diagnostic yields of 77.9%-94.4% with excellent safety. RBS integrate AI-driven navigation and real-time imaging, achieving biopsy success rates up to 98.8% and diagnostic yield over 90%, while minimizing pneumothorax risks. Despite these advancements, challenges remain in algorithm validation, data privacy, and ethical considerations. AI demonstrates transformative potential in standardizing bronchoscopy practices, expanding access to underserved regions, and advancing personalized treatments.
KEY MESSAGE: AI significantly enhances the diagnostic accuracy and procedural safety of IP across education, imaging, navigation, and robotic systems; despite ongoing challenges in algorithm validation, AI is propelling the field toward standardized practices and personalized medicine.