Idiopathic Pulmonary Fibrosis Prognosis

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Sharing Data is Essential for the Future of AI in Medical Imaging

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Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

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Deep Learning-based Quantification of Traction Bronchiectasis Severity for Predicting Outcome in Idiopathic Pulmonary Fibrosis

OSIC is mentioned in this research.
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A Deep Learning Algorithm for Predicting Disease Progression in Idiopathic Pulmonary Fibrosis

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The Automated e-Lung CT Algorithm is More Prognostic than Lung Function in Patients with Non-IPF Fibrotic ILD; A Validation Study

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Novel e-Lung CT Biomarkers Combine to Provide Higher Prognostic Discrimination than FVC in Patients with Non-IPF Fibrotic Interstitial Lung Disesase

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Shaping the Future in Rare Lung Diseases: From Imaging to Patient Management

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Surface Projection of CT Reconstructed Lung Volume for Regional Lung Mechanics Estimation: A Preliminary Analysis

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Development and Validation of a CT-based Deep Learning Algorithm to Augment Non-invasive Diagnosis of Idiopathic Pulmonary Fibrosis

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