Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge

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Metric learning guided sinogram denoising for cone beam CT enhancement

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Performance Evaluation and Validation of e-Lung in Non-IPF Fibrotic ILD

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Medical Image Segmentation Using DUCK-Net

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Utilization of Imaging and Clinical Data in Predicting Lung Capacity Progression in Patients with Fibrosis

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Machine Learning Classifier is Associated with Mortality in Interstitial Lung Disease: A Retrospective Validation Study Leveraging Registry Data

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Multi-modal Machine Learning Classifier for Idiopathic Pulmonary Fibrosis Predicts Mortality in Interstitial Lung Diseases (AJRCCM)

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Evaluating Radiomic-Derived Features Across Different Image Convolution Kernels for Predicting IPF Patient Survival

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Computed tomography machine learning classifier correlates with mortality in interstitial lung disease

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Towards the Adoption of Quantitative Computed Tomography in the Management of Interstitial Lung Disease

OSIC members discuss how quantitative CT software has huge potential to develop imaging biomarkers to improve the management of fibrosing ILDs, but optimizing their adoption will require technical and societal challenges to be addressed.
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