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Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting
Published on: June 23, 2023
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Identification of age-dependent features of human bronchi using explainable artificial intelligence
Hiroaki Ikushima1,2, Kazuhiro Usui1
1Department of Respiratory Medicine, NTT Medical Center Tokyo, Tokyo, Japan.
ERJ Open Research
|October 18, 2023
Summary
Researchers developed an AI model to assess bronchial age from bronchoscopy images, identifying bronchial bifurcation sites as key age-dependent features. This offers a new way to quantify age-related lung changes.
Area of Science:
- Pulmonary Medicine
- Artificial Intelligence
- Medical Imaging
Background:
- Aging causes organ changes, but quantitative tests for bronchial aging are lacking.
- Bronchoscopy images offer a potential data source for assessing age-related bronchial alterations.
Purpose of the Study:
- To identify age-dependent features in bronchi using explainable AI on bronchoscopy images.
- To develop a quantitative method for evaluating age-related changes in the bronchi.
Main Methods:
- Utilized 11,374 bronchoscopy images for training and testing deep learning models (CNNs).
- Calculated 'bronchial age' from images and correlated it with chronological age.
- Employed Grad-CAM to visualize age-dependent features identified by the AI model.
Main Results:
- Deep learning models, particularly EfficientNetB3, showed high correlation (0.9617) between bronchial and chronological age.
- Explainable AI (Grad-CAM) highlighted bronchial bifurcation sites as critical age-dependent features.
- No significant differences in age prediction discrepancies were found across different respiratory diseases or smoking histories.
Conclusions:
- Bronchial bifurcation sites are universal age-dependent features in the bronchi.
- This AI-driven approach provides a novel, quantitative method for assessing bronchial aging.
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