Background: Interstitial lung disease (ILD) is a major complication in systemic sclerosis (SSc) patients, associated with substantial morbidity and mortality. Functional, imaging, and clinical measures of lung involvement could be biased in SSc due to its systemic nature and extra-articular involvement (e.g., cardiac, musculoskeletal). Artificial intelligence (AI) reading of high-resolution computed tomography (HRCT) has emerged as a novel tool for the objective and reliable assessment of pulmonary diseases.

Objectives: The aim of this study is to correlate AVIEW measures, a deep learning based software for HRCT image assessment, with ILD-progression and disease-related mortality in SSc patients.

Methods: The AVIEW software (Coreline Soft, South Korea) was employed to analyze HRCT images from a cohort of consecutive SSc-ILD patients at baseline and after 24±3 months. Quantitative analyses included lung volume, texture, airways, and vascular anatomy. Baseline metrics were assessed for their association with ILD progression, defined by clinical, functional, and imaging criteria based on the INBUILD study parameters over 24 months. Furthermore, changes in AVIEW-derived measurements between two consecutive HRCT evaluations over the 24-month period were analyzed for their association with SSc-related mortality during the subsequent 36 months. All absolute measurements were normalized to body surface area.

Results: A total of 146 HRCT scans from 73 SSc-ILD patients were assessed (mean age 58.4±14.3 years; male 16.4%; diffuse skin variant 49.3%). Thirty-one patients (42.4%) experienced ILD progression over 24 months, which was predicted at baseline by higher percentages of ground glass opacities (GGO) (p=0.05) and reticulation (p=0.05), higher subpleural vessel volumes (p=0.017), and a tendency toward larger distal airways (p=0.066). Serial evaluations demonstrated that INBUILD progression was associated with a reduction in the percentage of normal lung (p=0.044) and absolute volumes (p=0.009), without significant changes in reticulation, GGO, vessels, or airways when considered individually. Twelve patients died due to SSc within 36 months following the second HRCT evaluation. Patients in the upper quartile for changes in reticular score and airway volume exhibited a higher mortality risk, independent of INBUILD progression (reticular score: OR 3.30, 95% CI 1.03–10.61, p=0.045; airway volume: OR 3.37, 95% CI 1.08–10.51, p=0.036).

Conclusion: Deep learning-based assessment in SSc-ILD identified distinct modifications in lung anatomical components with significant prognostic implications, potentially enabling more precise patient evaluation and stratification compared to conventional clinical tools.

REFERENCES: [1] De Lorenzis E, et al. Concordance and Prognostic Relevance of Different Definitions of Systemic Sclerosis Interstitial Lung Disease Progression. Am J Respir Crit Care Med. 2024.

[2] Flaherty KR, et al. Nintedanib in Progressive Fibrosing Interstitial Lung Diseases. N Engl J Med. 2019.

[3] Koh SY, et al. Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach. Eur Radiol. 2024.

Acknowledgements: NIL.

Disclosure of Interests: None declared.

© The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.