Charles River Analytics was awarded a ~$250K grant from the National Institutes of Health to develop Neurosurgical Planning with Robust Eloquent Area Delineation from Individualized Connectivity-Based Techniques (NeuroPREDICT). NeuroPREDICT is a software application that applies machine learning to medical imagery, enabling brain mapping of neurosurgical patients before surgery.
Overview of NeuroPREDICT’s data processing and machine learning pipeline
NeuroPREDICT will increase the accuracy of emerging brain-mapping techniques by using machine learning approaches to predict patients’ functional brain activation patterns from resting state data. NeuroPREDICT expands the applicability of pre-surgical brain mapping to more patients by eliminating the need to perform invasive, time-consuming, and technically demanding procedures before surgery.
“This application sits at the crossroads of medical imaging, medical software development, and advanced applied machine learning,” said Dr. Sean Tobyne, Physiological Systems Scientist at Charles River. “It has the potential to revolutionize pre-surgical brain mapping, making it cheaper, faster, and accessible to more patients, ultimately improving the outcomes of brain surgery and reducing post-operative deficits.”
Research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number R43NS117226. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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