SBIR Phase II: A Cognitive Dashboard to Support Clinical Decision Making in Neurosurgery

Award ID: 2444804

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will directly address the growing national economic and individual burden of brain diseases that require neurosurgery. Neurosurgical interventions to remove brain tumors or treat medically refractory epilepsy strive to not cause post-operative cognitive deficits in patients. However, a majority of neurosurgery patients self-report post-operative cognitive deficits affecting language, vision, hearing, touch or movement that lower quality of life and increase morbidity. By creating tools that improve outcomes, patients are able to return to their lives more completely after surgery, which has broad implications for not just their own quality of life, but that of their families, with direct consequences for the broader economy and society. This first-of-its-kind tool to support quantitative pre-operative surgical planning and evidence-based prediction of post-operative outcome will reduce uncertainty associated with neurosurgical interventions and relieve hospitals, healthcare payers, and patients of increased costs due to potential postoperative complications driven by neurocognitive deficits. This Small Business Innovation Research (SBIR) Phase II project will complete a novel suite of products that integrate multiple sources of information to enable clinicians to more effectively plan neurosurgery in support of the best post-operative quality-of-life for their patients. There is currently no publicly or commercially available software platform that integrates the administration, recording, analysis and cross-registration of behavioral tests and functional and structural brain mapping protocols. Clinical teams therefore devote an enormous amount of time to translating siloed streams of information into evidence that is relevant for clinical decision making. This project will directly address these technological shortcomings by utilizing a combination of Artificial Intelligence/Machine Learning and statistical models that integrate these clinical data and allow for predictive simulations of how a surgical plan will affect outcomes. Under the Phase II project, important user-interface and regulatory submission (510(k)) milestones will be completed, and the technical infrastructure for predicting patient outcomes will be validated using a closed historical dataset. Successful completion of this project will demonstrate prediction of post-operative quality of life and neurocognitive outcomes at levels that match or exceed reported precedents, and culminate in submission of a Class II De Novo device application to the FDA. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Henry Ahn