Advances in the application of deep learning in computed tomography imaging analysis of rare lung diseases
TL;DR
Deep learning applications across core stages of CT imaging analysis for rare respiratory diseases: image reconstruction and generation, lesion segmentation and detection, disease classification and diagnosis, and treatment response and prognosis prediction are summarized.
Abstract
Diagnosis and management of rare lung diseases remain challenging owing to their low incidence, heterogeneous manifestations, and limited therapeutic options. High-resolution computed tomography (CT) is central to imaging assessment, yet conventional visual interpretation is subjective and lacks good reproducibility. Recent advances in artificial intelligence, especially deep learning, provide new approaches for automated, quantitative and objective chest computed tomography image analysis. This review summarizes deep learning applications across core stages of CT imaging analysis for rare respiratory diseases: image reconstruction and generation, lesion segmentation and detection, disease classification and diagnosis, and treatment response and prognosis prediction. With typical cases of rare lung diseases, we illustrate that deep learning models enable accurate quantification of imaging biomarkers, elevated diagnostic accuracy and optimized outcome stratification. Despite notable progress, key challenges remain in model generalization, interpretability, and clinical validation.