In recent years, there have been significant technology innovations that are shaping the future of diagnostic imaging. AI as a supplemental lens for medical image analysis is on the rise, and its use has contributed to rapid developments in many disease states with regards to imaging-based diagnosis, prognosis and prediction of response to therapy.
But in order for AI to “do its job,” there needs to be enough data. And up until now, a major obstacle in harnessing this technology to study pulmonary fibrosis has been the lack of large, diverse imaging repositories needed to drive machine learning research. “Machine learning is the rocket, but the fuel is the data. Without that, we’re going nowhere,” said Dr. Simon Walsh, National Heart and Lung Institute, Imperial College London & OSIC radiology lead.
The OSIC Data Repository is changing all of that.
What happens when you bring together teams of global experts to examine the world’s largest and most diverse database for rare fibrotic lung diseases?
What is possible when the best and brightest radiologists, pulmonologists, machine learners, and imaging experts from industry and academia pool their collective brain power?
What useful metrics can members of this unique collaborative derive when they kick the tires of the first-of-its-kind global data repository for interstitial lung diseases?
What can they discover, together? We can’t wait to find out!
Collaboration and access to data help drive medical innovation. “The future of medical research depends heavily on our ability to collate significant amounts of data, and make that data available for detailed and open scientific investigation,” said Dr. David Barber, University College London & OSIC computational science lead. “It's a proud moment that OSIC is at the forefront of this movement.”
Launching the OSIC Data Repository has been a collaboration in its truest sense. By bringing together the world’s “best in class,” we’ve been able to build something truly remarkable – the largest and most diverse global database for rare fibrotic lung diseases. All open source. All for the benefit of patients.