Deep Learning-Based Airway Segmentation in Systemic Lupus Erythematosus Patients with Interstitial Lung Disease (SLE-ILD): A Comparative High-Resolution CT Analysis

2026-03-18

AI Summary

This study addresses the identification of airway structural alterations in patients with systemic lupus erythematosus-associated interstitial lung disease (SLE-ILD) at the lobar and segmental levels. Leveraging high-resolution computed tomography (HRCT) images, we developed a customized U-Net deep learning model to enable automated segmentation and quantitative volumetric analysis of airways within individual lung lobes and segments. For the first time using an AI-driven approach, we identified a regional airway dilation phenotype predominantly affecting the upper lung zones—specifically the right and left upper lobes and the R1, R3, and L3 bronchopulmonary segments—in SLE-ILD patients (p<0.05). These findings suggest that airway volume may serve as a potential imaging biomarker, offering a novel avenue for the early detection and monitoring of SLE-ILD.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Structured Learning
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

To characterize lobar and segmental airway volume differences between systemic lupus erythematosus (SLE) patients with interstitial lung disease (ILD) and those without ILD (non-ILD) using a deep learning-based approach on non-contrast chest high-resolution CT (HRCT). Methods: A retrospective analysis was conducted on 106 SLE patients (27 SLE-ILD, 79 SLE-non-ILD) who underwent HRCT. A customized deep learning framework based on the U-Net architecture was developed to automatically segment airway structures at the lobar and segmental levels via HRCT. Volumetric measurements of lung lobes and segments derived from the segmentations were statistically compared between the two groups using two-sample t-tests (significance threshold: p < 0.05). Results: At lobar level, significant airway volume enlargement in SLE-ILD patients was observed in the right upper lobe (p=0.009) and left upper lobe (p=0.039) compared to SLE-non-ILD. At the segmental level, significant differences were found in segments including R1 (p=0.016), R3 (p<0.001), and L3 (p=0.038), with the most marked changes in the upper lung zones, while lower zones showed non-significant trends. Conclusion: Our study demonstrates that an automated deep learning-based approach can effectively quantify airway volumes on HRCT scans and reveal significant, region-specific airway dilation in patients with SLE-ILD compared to those without ILD. The pattern of involvement, predominantly affecting the upper lobes and specific segments, highlights a distinct topographic phenotype of SLE-ILD and implicates airway structural alterations as a potential biomarker for disease presence. This AI-powered quantitative imaging biomarker holds promise for enhancing the early detection and monitoring of ILD in the SLE population, ultimately contributing to more personalized patient management.

Problem

• airway segmentation
• systemic lupus erythematosus
• interstitial lung disease
• high-resolution CT
• quantitative imaging biomarker

Innovation

• deep learning
• airway segmentation
• U-Net
• quantitative imaging biomarker
• high-resolution CT