Radiomics and Artificial Intelligence in Pulmonary Fibrosis

A scoping review was conducted to investigate the role of radiological imaging, particularly high-resolution computed tomography (HRCT), and artificial intelligence (AI) in diagnosing and prognosticating idiopathic pulmonary fibrosis (IPF). Relevant studies from the PubMed database were selected based on predefined inclusion and exclusion criteria. Two reviewers assessed study quality and analyzed data, estimating heterogeneity and publication bias. The analysis primarily focused on deep learning approaches for feature extraction from HRCT images, aiming to enhance diagnostic accuracy and efficiency. Radiomics, utilizing quantitative features extracted from images, were computed using various tools to improve precision in analysis. Validation methods such as k-fold cross-validation were employed to assess model robustness and generalizability. Findings revealed that radiologic patterns in interstitial lung disease hold prognostic significance for patient survival. However, the additional prognostic value of quantitative assessment of fibrosis extent remains uncertain. IPF poses a substantial challenge in respiratory medicine, necessitating advanced diagnostic and prognostic tools. Radiomics emerges as a valuable asset, offering insights into disease characteristics and aiding in disease classification. It contributes to understanding underlying pathophysiological processes, facilitating more effective management of pulmonary disorders. Future research should focus on clarifying the additional prognostic value of quantitative assessment and further refining AI-based diagnostic and prognostic models for IPF.