Predicting Risk of Pulmonary Fibrosis Formation in PASC Patients

2025-05-15

AI Summary

This study addresses the challenge of predicting pulmonary fibrosis risk in patients with post-acute sequelae of SARS-CoV-2 infection (PASC). We propose the first explainable deep learning framework specifically designed for multicenter chest CT imaging. Methodologically, we integrate an end-to-end convolutional neural network with quantitative radiomic features and incorporate Grad-CAM visualization to enable lesion localization and traceable decision rationale. Critically, this work pioneers the synergistic integration of deep learning and interpretable radiomics for PASC-specific pulmonary fibrosis risk stratification. Evaluated on a multicenter dataset, our framework achieves 82.2% accuracy and an AUC of 85.5%, significantly outperforming conventional radiological assessment. It constitutes the first computationally driven, interpretable, and deployable early-warning tool for clinical use—enabling timely, precision interventions and individualized patient management.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Algorithmic accountability and transparency on the web

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.

Problem

• Predicting pulmonary fibrosis risk in Long COVID patients
• Developing CT analysis for early fibrosis detection
• Improving PASC lung damage assessment accuracy

Innovation

• Deep learning and radiomics for fibrosis prediction
• Convolutional neural networks for feature extraction
• Grad-CAM visualization for clinical insights