Deep learning-Based Airway and Vessel Volume are Associated with Increased Risk of Mortality in Connective Tissue Disease-Related Interstitial Lung Disease. - Amsterdam 2025

Advancing pulmonary imaging biomarkers: quantitative computed tomography, deep learning and structural insights across lung diseases

Chair(s): Sara Piciucchi, Ádám Domonkos Tárnoki

Session type: Oral presentation

Deep learning-Based Airway and Vessel Volume are Associated with Increased Risk of Mortality in Connective Tissue Disease-Related Interstitial Lung Disease.

Simon Walsh

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Late Breaking Abstract - AI-powered quantification of ILD patterns on CT for progression evaluation in SSc-ILD patients

Flore Belmans

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Abstract

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Deep Learning-based Fibrosis and Ground Glass Opacification Quantification with Proteomic Biomarkers for Outcome Prediction in Idiopathic Pulmonary Fibrosis.

Simon Walsh

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Poster

Abstract

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Deep learning-Based Airway and Vessel Volume are Associated with Increased Risk of Mortality in Connective Tissue Disease-Related Interstitial Lung Disease.

Simon Walsh

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Poster

Abstract

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In vivo CT-based emphysema and micro-CT-based mean linear intercept: Bridging the gap

Jonas Van Der Rauwelaert

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Poster

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Quantitative CT biomarkers reveal COPD subtypes with distinct lung function profiles

Juan Domingo Gispert

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Poster

Abstract

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Automated analysis of airway remodeling and emphysema across GOLD Stages: Insights in COPD Severity

Tjeerd Van Der Veer

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Poster

Abstract

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Computational Fluid Dynamics analysis on total airway casts to explore airflow parameters in COPD and healthy

Yousry Khaled Mohamady

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Abstract

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Prevalence of structural lung abnormalities in a large pediatric bronchopulmonary dysplasia cohort

Merlijn Bonte

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Poster

Abstract

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Role of the Fibroblast Activation Protein as biomarker of fibrotic lung diseases: interim analysis of a prospective exploratory multi-cohort study

Anne-leen Deleu

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Poster

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Discussion and Q&A

CDN Connection Issue

The ERS Congress 2025, Amsterdam

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