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Elizabeth Estes, The Open Source Imaging Consortium (OSIC) Executive Director, discusses adding X-Rays to the OSIC Data Repository and its resulting potential for quantifying respiratory diseases. Medicom Technologies is partnering with the Open Source Imaging Consortium (OSIC) to help continue to add anonymized, comprehensive data sets to the OSIC Data Repository.
Open Source Imaging Consortium (OSIC) Member CSL Behring hosted a symposium during the European Respiratory Society (ERS) International Congress which included presentations from several internationally renowned experts in rare lung diseases. The panel discussed the need to improve clinical decision making to expedite disease recognition, prognostic prediction, and early treatment in interstitial lung disease (ILD) and alpha 1 antitrypsin (AAT) deficiency-related chronic obstructive pulmonary disease (COPD).
Radiologists are teaming up to gather data on IPF, a deadly lung disease, utilizing AI to more quickly diagnose and treat those who are affected.
Could a repository of anonymized CT scans and clinical information provide critical clues about rare, unclassified lung diseases? Elizabeth Estes, Executive Director of The Open Source Imaging Consortium (OSIC), and Dr. Simon Walsh, consultant radiologist and NIHR clinician scientist, sure think so.
In this episode, Elizabeth and Dr. Walsh discuss the exciting role machine learning and algorithms may play on enhancing our disease knowledge—from diagnosis and prognosis to biomarker discovery and therapy response. The Open Source Image Consortium (OSIC) is working to democratize medicine by giving OSIC clinicians and members everywhere the ability to access and benefit from the same technology and information as those affiliated with major research centers. “If we can figure out how to drive collaboration in healthcare, we will change the paradigm,” says Executive Director Elizabeth Estes.
A Bold Approach: Improving Rare Disease Treatment and Patient Care through Data Transparency3/11/2022
For decades, the healthcare industry has lacked a long list of elements necessary to understand the nature of hundreds of rare diseases — industry cohesiveness and data transparency chief amongst them. With the help of PwC and Microsoft, one pioneering group of minds may have finally found a key part of the solution: an open-source approach to medical research.
“We have a lot of smart, motivated, dedicated people together who want to see these patients have a different path. The technology is there for personalized medicine. The technology is there to make advances in rare disease,” says Elizabeth Estes, Open Source Imaging Consortium (OSIC) Executive Director.
A discussion with The Open Source Imaging Consortium (OSIC), Microsoft and PwC: See how data and analytics, AI and cloud will reshape the future of healthcare.
A first-of-its-kind open source medical imaging and data repository platform is highlighting new possibilities to help improve the speed and accuracy of diagnosis and help patients, providers and researchers better manage the disease.
A large and multi-ethnic database, reported to be the first of its kind for rare lung diseases, is now compiling real-world clinical and imaging data on people with pulmonary fibrosis (PF) and other interstitial lung diseases (ILDs) from centers across the globe.
The highly-anticipated Open Source Imaging Consortium (OSIC) database is driven by global experts in pulmonology, radiology and artificial intelligence, and is the most diverse and largest for rare fibrotic lung diseases HOLLAND, Mich., Tuesday, September 7, 2021 – The Open Source Imaging Consortium (OSIC) today announced the launch of its global, data-rich repository of anonymized HRCT scans and clinical information regarding interstitial lung diseases (ILDs). This first-of-its-kind database is the world’s largest and most diverse, with a plethora of real world clinical and imaging data that is both multi-ethnic and multi-center. The OSIC Data Repository currently houses close to 1,500 anonymized and quality-controlled scans with accompanying data, and has an additional 5,000 in the quality control queue. It is on track to reach its goal of 15,000 anonymized scans, available to OSIC members, by first quarter 2022.
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November 2024
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