Radiomics-Based Artificial Intelligence and Machine Learning Approach for the Diagnosis and Prognosis of Idiopathic Pulmonary Fibrosis: A Systematic Review

Abstract

Idiopathic pulmonary fibrosis (IPF) is a devastating interstitial lung disease (ILD) characterized by progressive fibrosis and poor survival outcomes. Accurate diagnosis and prognosis remain challenging due to overlapping features with other ILDs and variability in imaging interpretation. This systematic review evaluates the current evidence on artificial intelligence (AI) and machine learning (ML) applications for the diagnosis and prognosis of IPF using computed tomography (CT) imaging. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, eight studies published between 2017 and 2024 were included, demonstrating promising results across various methodologies, including deep learning (DL) models, support vector machines (SVMs), and ensemble approaches. AI-derived parameters, particularly measures of fibrotic burden and pulmonary vascular volume, consistently outperformed conventional visual CT scores for prognostication. Strong correlations between AI-quantified CT features and pulmonary function (PF) tests suggest potential surrogate markers for physiological parameters. Novel prognostic biomarkers identified through AI analysis expand understanding beyond traditional parenchymal assessment. Despite these advances, limitations include retrospective designs, sample size constraints, male-predominant cohorts, and limited external validation. Future research should prioritize large, prospective, multi-center studies with diverse populations, standardized protocols, explainable AI (XAI) techniques, and integration into clinical workflows to realize the transformative potential of AI for improving IPF management.