Accelerating Clinical Research with Automated Workflows for Personalized TMS Treatment
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Clinical research is generating increasing volumes of imaging data, but finding the resources to efficiently organize, curate, and share patient data for research can be challenging. 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, Dr. Azeezat Azeez, a Postdoctoral Research Fellow at Stanford, outlines her approach to automating research workflows that help clinicians accelerate their research and treatment planning. Dr. Azeez is 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 Dr. Azeez and her colleagues:
- Collected and instantly shared fMRI 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