• OSIC Published Research

OSIC has been featured in numerous posters and presentations at leading conferences and scientific events, including the American Thoracic Society (ATS), the Radiological Society of North America (RSNA), the American College of Chest Physicians (CHEST), the IEEE Conference on Artificial Intelligence (IEEE CAI), the AI & Machine Learning Summit, the International Conference on Machine Learning (ICML 2025), the IPF Summit, and AWS re:Invent, among others in the United States.

Fibrotic and vascular abnormalities quantified by an AI-based model are associated with outcomes in patients with idiopathic pulmonary fibrosis

Data from the OSIC Data Repository was used to conduct this research.

READ MORE ON BMC PULMONARY MEDICINE

Disentanglement of Biological and Technical Factors via Latent Space Rotation in Clinical Imaging Improves Disease Pattern Discovery

Data from the OSIC Data Repository was used to conduct this research.

READ MORE ON ARXIV

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

OSIC is mentioned in this research.

READ MORE ON RESPIRATORY INNOVATION

Prognostication in patients with idiopathic pulmonary fibrosis using quantitative airway analysis from HRCT: a retrospective study

Data from the OSIC Data Repository was used to conduct this research.

READ MORE ON EUROPEAN RESPIRATORY JOURNAL

A multi-stage 3D convolutional neural network algorithm for CT-based lung segment parcellation

Data from the OSIC Data Repository was used to conduct this research.

READ MORE ON JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS

Uncertainty-based Interactive 3D Medical Image Segmentation

Data from the OSIC Data Repository was used to conduct this research.

READ MORE

Effect of Slice Thickness on Quantitative Analysis of Interstitial Lung Disease: A Retrospective Volumetric Chest CT Study

OSIC is mentioned in this research.

READ MORE ON LA RADIOLOGIA MEDICA

E-Lung Biomarkers are Associated with Future Progressive Pulmonary Fibrosis Independent of UIP Status on CT

Data from the OSIC Data Repository was used to conduct this research.

READ MORE ON AJRCM

Single Timepoint Prediction Score on High-resolution CT: Association with Progression-free Survival Interstitial Lung Disease

Data from the OSIC Data Repository was used to conduct this research.

READ MORE ON AJRCCM

Multimodal Machine Learning for Prognostic Modelling in Idiopathic Pulmonary Fibrosis

Data from the OSIC Data Repository was used to conduct this research.

READ MORE ON UCL DISCOVERY

Topics