ArticleRespiratory research2025
Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients.
Article in Respiratory research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Toward precision imaging in interstitial lung disease: advances in quantitative imaging and artificial intelligence.Current opinion in pulmonary medicine · 2026Review
- Computational interpretation of modified lung ultrasound performance in CTD-ILD.Clinical rheumatology · 2026Article
- Evolving landscape of imaging-based evaluation in systemic autoimmune rheumatic disease-associated interstitial lung disease: from visual assessment to quantitative artificial intelligence-assisted evaluation.Journal of rheumatic diseases · 2026Review
- AI in radiology and interventions: a structured narrative review of workflow automation, accuracy, and efficiency gains of today and what's coming.International journal of computer assisted radiology and surgery · 2026Review
- Artificial Intelligence Enabled Lung Sound Auscultation in the Early Diagnosis and Subtyping of Interstitial Lung Disease.Journal of clinical medicine · 2025Review
- Interstitial Lung Disease Outcome Prediction Using Quantitative Densitometry Indices on Baseline Chest Computed Tomography.Diagnostics (Basel, Switzerland) · 2025Article
- The association of symptoms, pulmonary function test and computed tomography in interstitial lung disease at the onset of connective tissue disease: an observational study with artificial intelligence analysis of high-resolution computed tomography.Rheumatology international · 2025Observational
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Authors and funding
22 authors.
Funding
Abstract
backgroundSystemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD.
methodsWe evaluated the potential of an automated ILD quantification system (icolung
resultsWe evaluated a group of 75 patients suffering from SSc-ILD, either limited or diffuse, of whom 30 presented progressive pulmonary fibrosis (PPF). The patients presenting PPF exhibited more extensive lesions (in % of total lung volume (TLV)) based on image analysis than those without PPF: 3.93 (0.36-8.12)* vs. 0.59 (0.09-3.53) respectively, whereas pulmonary functional test showed a reduction in Force Vital Capacity (FVC)(pred%) in patients with PPF compared to the others : 77 ± 20% vs. 87 ± 19% (p < 0.05). Modifications of FVC and diffusing capacity of the lungs for carbon monoxide (DLCO) over time were correlated with longitudinal radiological ILD modifications (r=-0.40, p < 0.01; r=-0.40, p < 0.01 respectively).
conclusionAI-based automatic quantification of lesions from chest-CT images in SSc-ILD is correlated with physiological parameters and can help in disease evaluation. Further clinical multicentric validation is necessary in order to confirm its potential in the prediction of patient's outcome and in treatment management.
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