ArticleTherapeutic advances in chronic disease2023
Distinguishing bronchoscopically observed anatomical positions of airway under by convolutional neural network.
Article in Therapeutic advances in chronic disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Artificial intelligence in bronchoscopy: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Artificial Intelligence in Pediatric Bronchoscopy: Current Evidence and Future Perspectives.Pediatric pulmonology · 2026Review
- Review
- Tumor detection on bronchoscopic images by unsupervised learning.Scientific reports · 2025Article
- Artificial Intelligence in Interventional Pulmonology.Therapeutic advances in pulmonary and critical care medicineReview
- Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images.Therapeutic advances in respiratory diseaseArticle
- Science progress distinguishing different types of airway stents under bronchoscopy by artificial intelligence.Science progressArticle
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) technology has been used for finding lesions Objectives: To use convolutional neural network (CNN) to recognize the observed anatomical positions of the airway under bronchoscopy. Design: We designed the study by comparing the imaging data of patients undergoing bronchoscopy from March 2022 to October 2022 by using EfficientNet (one of the CNNs) and U-Net. Methods: Based on the inclusion and exclusion criteria, 1527 clear images of normal anatomical positions of the airways from 200 patients were used for training, and 475 clear images from 72 patients were utilized for validation. Further, 20 bronchoscopic videos of examination procedures in another 20 patients with normal airway structures were used to extract the bronchoscopic images of normal anatomical positions to evaluate the accuracy for the model. Finally, 21 respiratory doctors were enrolled for the test of recognizing corrected anatomical positions using the validating datasets. Results: In all, 1527 bronchoscopic images of 200 patients with nine anatomical positions of the airway, including carina, right main bronchus, right upper lobe bronchus, right intermediate bronchus, right middle lobe bronchus, right lower lobe bronchus, left main bronchus, left upper lobe bronchus, and left lower lobe bronchus, were used for supervised machine learning and training, and 475 clear bronchoscopic images of 72 patients were used for validation. The mean accuracy of recognizing these 9 positions was 91% (carina: 98%, right main bronchus: 98%, right intermediate bronchus: 90%, right upper lobe bronchus: 91%, right middle lobe bronchus 92%, right lower lobe bronchus: 83%, left main bronchus: 89%, left upper bronchus: 91%, left lower bronchus: 76%). The area under the curves for these nine positions were >0.98. In addition, the accuracy of extracting the images Conclusion: Our study proved that AI technology can be used to distinguish the normal anatomical positions of the airway, and the model we trained could extract the corrected images
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