Preclinical Stroke

Preclinical stroke research utilizes rodent models and advanced imaging techniques like MRI to evaluate potential cerebroprotectant therapies and understand stroke pathophysiology. Current efforts focus on developing automated image analysis pipelines, employing deep learning architectures such as U-nets and transfer learning methods, to efficiently and accurately quantify lesion volume, brain atrophy, and other key metrics across large, multi-site studies. This improved quantification and standardization of preclinical stroke assessment promises to enhance the reliability and translational potential of future therapeutic trials, ultimately improving the development of effective stroke treatments.