Cover Image

Deep learning-based quality control and diagnosis of bronchial images

Background: 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...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhou, Yong (Author) , Herth, Felix (Author) , Liu, Bin (Author) , Li, Fengjuan (Author) , Ruan, Chao (Author) , Cai, Huafeng (Author) , Li, Yuchen (Author) , Li, Jianying (Author)
Format: Article (Journal)
Language:English
Published: November 27, 2025
In: Respiration
Year: 2025, Pages: ?
ISSN:1423-0356
DOI:10.1159/000548342
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1159/000548342
Verlag, lizenzpflichtig, Volltext: https://karger.com/res/article-abstract/doi/10.1159/000548342/940096/Deep-Learning-Based-Quality-Control-and-Diagnosis?redirectedFrom=fulltext
Get full text
Author Notes:Yong Zhou, Felix J.F. Herth, Bin Liu, Fengjuan Li, Chao Ruan, Huafeng Cai, Yuchen Li, Jianying Li
Description
Summary:Background: 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. Summary: 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. Key Messages: 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.
Item Description:Gesehen am 16.03.2026
Physical Description:Online Resource
ISSN:1423-0356
DOI:10.1159/000548342

Similar Items