In this episode, I talk with Emi Gal, co-founder and CEO of Ezra, about cancer screening with a full body MRI scan. Ezra is on a mission to detect cancer early for everyone by making the process more accurate, faster, and cheaper. Emi and I talked about the challenges in working with MR data, how regulatory processes affect model development, and the importance of validation.

“What we've been able to achieve is to essentially reduce the cost and the time in a scanner of an MRI from about two to three hours for full body to 60 minutes. And we're actually working on a new AI that will roll out next year that will reduce the scan time to 30 minutes.”

“What we do is we acquire the scan fewer times, and then we've built machine learning models that recognize what noise looks like and then just remove that noise. And then we kind of expanded that from not just noise. If you acquire scans with lower resolution, the resulting images are a little bit blurry so we can sharpen them.”

“Our focus on the scanning front is to reduce scan time, which yields these images with increased noise artifacts, and then use machine learning to enhance these images so that a radiologist can then use them for interpretation.”

“I think what having to receive FDA clearance for AI does, is it really forces the company from day one to think about what are all of the things that might influence the performance of said AI, and what can we do to ensure that we maximize the chances of success?”

“We have had an instance when we had to go back to the drawing board and build the model again because we failed internal validation prior to formal validation that we had to submit to the FDA.”

“I think the way you ensure that the technology we develop fits the clinical workflow is actually not starting with the technology, but starting with the end goal in mind and then figuring out what you need to do in order to achieve that.”

“To screen a hundred million people a year, we think, is a huge endeavor and probably going to take a decade or two to achieve. And I'm personally committed to Ezra for the rest of my career.  In the next three to five years, I would hope we are making good progress towards that mission, and maybe in five years we're screening at least a million people a year.”


Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.
Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.
Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.
Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.