This review evaluates computational and AI-based methods for quantifying interstitial lung disease (ILD) on HRCT in patients with inflammatory rheumatic diseases (IRD), covering technical approaches, validation strategies, and clinical applications.
Computational HRCT analysis enables automated, objective volumetric assessment of parenchymal changes and pattern classification with greater accuracy and efficiency than traditional visual scoring, addressing key limitations like inter-observer variability and expert time burden.
As a narrative review, it does not provide pooled quantitative data or meta-analytic results; the breadth of included technical approaches may limit depth on any single method; clinical validation across diverse IRD subtypes remains incompletely characterized.
Clinicians managing IRD-ILD should be aware that AI-driven HRCT quantification tools are maturing and may soon supplement or reduce reliance on subjective visual scoring. Familiarity with these tools' validation status is important before adopting them in clinical decision-making.
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