Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis - Milan 2023

Personalised medicine and deep learning in interstitial lung disease and chronic obstructive pulmonary disease imaging

Chair(s): Adam Domonkos Tarnoki, Lucio Calandriello

Session type: Oral presentation

Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis

Federico Felder

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Late Breaking Abstract - Artificial intelligence-based decision support for HRCT stratification in fibrotic lung disease; an international study of 116 observers from 37 countries.

Lucio Calandriello

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Poster

Abstract

Slides

Alveolar collapse as a prognostic marker in patients with IPF: A CT-based assessment using an extended parametric response mapping technique

Sarah Scharm

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Poster

Abstract

Slides

In-vivo endobronchial-PS-OCT for fibrosis quantification in ILD

Tatiana Soldati

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Poster

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Prognostic modeling in idiopathic pulmonary fibrosis using deep learning

Alexandre Coimbra

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Poster

Abstract

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A deep learning algorithm for predicting disease progression in idiopathic pulmonary fibrosis

Simon Walsh

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Poster

Abstract

Slides

Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis

Federico Felder

Replay

Poster

Abstract

Slides

Comparison of parenchyma density scoring in RA-ILD vs IPF and iNSIP

Balint Acs

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Abstract

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Endobronchial polarization sensitive optical coherence tomography for airway wall imaging

Sofi Vassileva

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Poster

Abstract

Slides

Towards precision medicine in COPD: Bioradiomic SPECT-CT quantification of active lung inflammation

Benjamin Welham

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Poster

Abstract

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Impaired xenon gas transfer observed two years after COVID-19 hospitalisation in patients with signs of interstitial lung disease

Helen Marshall

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Poster

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Discussion and Q&A

CDN Connection Issue

The ERS International Congress 2023, Milan

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