SPAIDNet: a Deep Learning-Based Imaging Classification Framework for Interstitial Lung Disease Developed and Validated in A Multicenter Prospective Cohort Study - Amsterdam 2025

Technology and imaging in interstitial lung disease/Clinical aspects in interstitial lung disease

Chair(s): Punchalee Kaenmuang, Diana Calaras

Session type: Poster session

SPAIDNet: a Deep Learning-Based Imaging Classification Framework for Interstitial Lung Disease Developed and Validated in A Multicenter Prospective Cohort Study

Hongyi Wang

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The need for genetic consultation in lung transplantation for interstitial lung diseases

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Role of 3-D Echo in Predicting Pulmonary Vascular Damage in IPF: a prospective study

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The initial CT can predict progressive pulmonary fibrosis in unclassifiable idiopathic interstitial pneumonias

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Deep learning analysis of serial CT scans correlates with changes in how patients feel, function and survive in fibrotic interstitial lung disease

Giles Dixon

Poster

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Developing and Validation of a Multimodal-based Machine Learning Model for Diagnosis of Usual Interstitial Pneumonia: a Prospective Multicenter Study

Hongyi Wang

Poster

Abstract

SPAIDNet: a Deep Learning-Based Imaging Classification Framework for Interstitial Lung Disease Developed and Validated in A Multicenter Prospective Cohort Study

Hongyi Wang

Poster

Abstract

A CT imaging measure of combined pulmonary fibrosis and emphysema (CPFE)

Jennifer Wang

Poster

Abstract

SPECT-CT imaging of avb3 integrin reveals increased activity in patients with fibrotic interstitial lung disease compared to healthy controls

Giles Dixon

Poster

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Late Breaking Abstract - Safety and tolerability of nerandomilast in patients with pulmonary fibrosis: pooled data from the FIBRONEER-IPF and FIBRONEER-ILD trials

Justin M Oldham

Poster

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CDN Connection Issue

The ERS Congress 2025, Amsterdam

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