Predicting Risk of Pulmonary Fibrosis Formation in PASC Patients
Journal:
arXiv
Published Date:
May 15, 2025
Abstract
While the acute phase of the COVID-19 pandemic has subsided, its long-term
effects persist through Post-Acute Sequelae of COVID-19 (PASC), commonly known
as Long COVID. There remains substantial uncertainty regarding both its
duration and optimal management strategies. PASC manifests as a diverse array
of persistent or newly emerging symptoms--ranging from fatigue, dyspnea, and
neurologic impairments (e.g., brain fog), to cardiovascular, pulmonary, and
musculoskeletal abnormalities--that extend beyond the acute infection phase.
This heterogeneous presentation poses substantial challenges for clinical
assessment, diagnosis, and treatment planning. In this paper, we focus on
imaging findings that may suggest fibrotic damage in the lungs, a critical
manifestation characterized by scarring of lung tissue, which can potentially
affect long-term respiratory function in patients with PASC. This study
introduces a novel multi-center chest CT analysis framework that combines deep
learning and radiomics for fibrosis prediction. Our approach leverages
convolutional neural networks (CNNs) and interpretable feature extraction,
achieving 82.2% accuracy and 85.5% AUC in classification tasks. We demonstrate
the effectiveness of Grad-CAM visualization and radiomics-based feature
analysis in providing clinically relevant insights for PASC-related lung
fibrosis prediction. Our findings highlight the potential of deep
learning-driven computational methods for early detection and risk assessment
of PASC-related lung fibrosis--presented for the first time in the literature.
