Recent Advances in Imaging Analysis for Interstitial Lung Disease

Interstitial lung disease (ILD) encompasses a heterogeneous group of pulmonary disorders with variable etiologies, clinical courses, and prognoses. Recent advances in imaging analysis, particularly automated quantification and artificial intelligence-based technologies, have significantly enhanced diagnostic precision and prognostic modeling. Quantitative high-resolution computed tomography allows objective assessment of disease extent, pattern classification, and regional distribution of fibrotic lesions, providing essential information for staging and treatment decisions. Deep-learning-based segmentation and pattern recognition techniques can extract high-dimensional imaging features, facilitating phenotype clustering, risk stratification, and longitudinal monitoring. Recent efforts to integrate imaging data with clinical parameters and multi-omics profiles have further advanced the field of precision medicine. This review discusses the current state of imaging analysis technologies for ILD, emphasizing clinical applications, disease-specific use cases, and emerging directions for biomarker discovery and individualized patient care.