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If we can catch arthritis earlier in patients, then ultimately we should be able to cure it
Drs. Igor Jurisica and Christian Veillette
Drs. Igor Jurisica (left) and Christian Veillette, two leading AI researchers, share their thoughts on how the technology is being used to better treat arthritis.

Drs. Igor Jurisica and Christian Veillette hope to improve treatments and eventually stop arthritis from developing in patients. How? With data and analysis

For Drs. Igor Jurisica and Christian Veillette, AI isn’t just a buzzword. The two have been using AI and machine learning algorithms to analyze arthritis-related information and create rich data sets, which will help doctors diagnose arthritis earlier and treat the disease more effectively.

Q: DR. JURISICA, YOU BEGAN YOUR CAREER USING PREDICTIVE ANALYTICS

FOR CANCER RESEARCH. WHAT DREW YOU TO FOCUS ON ARTHRITIS?

Dr. Igor Jurisica: With cancer, we usually have one sample per patient, and we're trying to predict where it's coming from and where it's going. With arthritis we have at least a few samples, so we can, on a molecular level, analyze where the disease was before surgery, what changes were made during the surgery and how it's changing as a response to treatment over time. We started to develop computational tools to analyze these individual data sets, which gives us so much more opportunity to study what happens to these patients in their recovery, and long term, it can help us personalize treatments for individuals.

Dr. Christian Veillette: It was our samples that caught Igor's attention. We have a large and well-documented repository for clinical samples – called a biobank – that we've built up over the years, using pre- and post-op samples from arthritis patients. We have tissues from the hand, wrist, knee and spine, including synovial fluid, blood samples and more. We've amassed thousands of samples, and the collection continues to expand in numbers and richness. It's really incredible to have access to such a wealth of high-quality data. You don't have as much data with cancer, and especially not longitudinal samples from the same patient.