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Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airways Disease.
Di Zhang1, Mingyue Zhao2,3, Xiuxiu Zhou1
1Department of Radiology, Changzheng Hospital, Naval Medical University, 415 Fengyang Rd, Shanghai 200003, The People's Republic of China.
Radiology. Artificial Intelligence
|July 16, 2025
Summary
A new deep learning model can accurately predict small airways disease using a single CT scan, matching the performance of traditional methods. This advancement aids in diagnosing lung conditions like COPD.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Medicine
- Pulmonary Medicine
Background:
- Functional small airways disease (fSAD) is a key indicator of early lung disease, often challenging to diagnose with standard imaging.
- Parametric Response Mapping (PRM) is a validated technique for assessing lung disease but typically requires paired inspiratory and expiratory CT scans.
- Developing methods to predict fSAD and perform PRM using single-view CT scans can improve diagnostic efficiency and accessibility.
Purpose of the Study:
- To develop and validate a deep learning (DL) model capable of performing PRM and predicting fSAD from a single inspiratory chest CT scan.
- To compare the performance of generative and predictive DL models in this task.
- To assess the model's generalizability across different test datasets, including an external validation set.
Main Methods:
- Retrospective development of predictive and generative DL models for PRM using inspiratory CT scans.
- Fivefold cross-validation on a model development dataset, with PRM from paired respiratory CT serving as the reference standard.
- Evaluation using voxelwise metrics (sensitivity, AUC, SSIM) and testing on internal and external datasets.
Main Results:
- The generative DL model significantly outperformed the predictive model in detecting fSAD (sensitivity 86.3% vs 38.9%; AUC 0.86 vs 0.70).
- The generative model demonstrated robust performance across multiple internal and external test sets, with AUCs for fSAD detection ranging from 0.83 to 0.85.
- Exceptional performance was noted in the preserved ratio impaired spirometry group (AUC 0.88 for fSAD).
Conclusions:
- A generative DL model utilizing a single inspiratory CT scan can effectively perform PRM and predict fSAD.
- This approach achieves performance comparable to traditional methods requiring paired CT scans.
- The developed model shows promise for improving the diagnosis and management of lung diseases like COPD.
