Beyond Visual Scoring: Computational <scp> <b>CT</b> </scp> ‐analysis for <scp> <b>HRCT</b> </scp> based quantification of Interstitial Lung Disease in Inflammatory Rheumatic Disease

Interstitial lung disease (IRD-ILD) is a significant cause of morbidity and mortality in patients with inflammatory rheumatic disorders (IRD). High-resolution computed tomography (HRCT) is widely considered the gold standard for the non-invasive assessment of ILD; however, its interpretation is constrained by substantial inter-observer variability and the need for time-consuming expert evaluation. Computer-based image analysis including artificial intelligence (AI) has emerged as a promising approach for the automated, objective, and quantitative analysis of HRCT images. This method enables precise volumetric assessment of parenchymal alterations and facilitates pattern classification with unprecedented accuracy and efficiency. This review synthesises current evidence on computational driven HRCT quantification in IRD-ILD, highlighting available technical approaches, validation strategies, and clinical applications.