Stanford Medicine's Brain Stimulation Lab is managing large volumes of complex psychiatric data in its research and clinical trials on treatment-resistant depression. Typical processing workflows for managing this volume and complexity of data are manual and time-consuming. The lab's work with vulnerable patient populations demands a faster and more reliable approach.
In this webinar from February 24, Azeezat Azeez, PhD, a postdoctoral research fellow at Stanford, will outline her approach to automating research workflows that help clinicians accelerate their research and treatment planning. Azeez will be joined by Andrew Geoly, an assistant clinical research coordinator at Stanford; and Michael Perry of Flywheel, a next-generation research data platform.
In this webinar, you'll learn how Azeez and her colleagues did the following:
- Collected and instantly shared functional MRI data to plan individualized treatments
- Developed reusable algorithms to standardize data for analysis and collaboration
- Reduced data processing timelines from days to hours
- Tracked all data and computation in a centralized, web-based platform
Click on the link below to view the video.
Flywheel Accelerating Clinical Research with Automated Workflows for Personalized Transcranial Magnetic Stimulation Treatment
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