High-quality? Highly sensitive
For over 60 years, the AO Foundation has been pushing the boundaries of medical research. With more than 4,500 patients enrolled in studies, and a global community of some 460,000 professionals, AO is at the very forefront of the intersection between healthcare and technology.
And that pioneering spirit made Zühlke a natural fit when the AO Foundation was looking for a technical partner on a new project: exploratory research into how AI modelling can improve spinal CT scans.
The challenge? To combine and use typically disparate datasets – from a raft of clinics and hospitals – without compromising patient data.
‘High-quality patient data is the basis for training artificial intelligence for any medical application’, explains AO’s Head of Technology Transfer, Roland Herzog. ‘That data is usually available in a given hospital’s local files, but a number of laws and data privacy regulations have to be followed if you want to access or use it. And hospitals are generally very reluctant to grant that access’.
A complex web of patient consent, clinical trial contracts, and regulatory red tape often makes taking datasets out of local repositories an impossible task. But a lack of usable data creates a barrier to advancements in AI modelling that could help automate some of the heavy lifting in CT scan diagnoses.
So, using a combination of next-generation machine learning techniques and a better-connected data ecosystem, that was exactly the barrier we at Zühlke set out to tackle.