Federal Project Grant Award Summary

Yale University received a $722,338 Project Grant from the National Institute of Neurological Disorders and Stroke (NINDS) under the Extramural Research Programs in the Neurosciences and Neurological Disorders program (CFDA 93.853), awarded on March 24, 2026, with a completion date of February 28, 2031. The award funds the development of automated, scalable clinical tools for post-traumatic epilepsy (PTE) risk stratification in traumatic brain injury (TBI) survivors. The project delivers three primary products: (1) an optimized electronic health record (EHR) phenotyping algorithm that leverages machine learning and natural language processing to retrospectively identify PTE patients and predict PTE risk using clinical data from the first seven days post-TBI; (2) a deep learning convolutional neural network (CNN) implementation to automatically quantify contusion volumes and locations from acute CT scans; and (3) cortical volume and thickness assessment tools derived from magnetic resonance imaging (MRI) analysis.

The research addresses critical gaps in existing PTE prediction models by automating labor-intensive data extraction processes and enabling scalability across larger health systems and populations. By analyzing approximately 3,000 TBI survivor cases, the project identifies novel clinical and imaging predictors to support early intervention and therapeutic planning. This automated, multidimensional approach aims to reduce health disparities in PTE care and enhance treatment feasibility across diverse patient populations.