Nerve artificial intelligence (nerveai) and high-resolution magnetic resonance neurography: advancing the diagnosis and treatment of headache disorders

The National Institute of Neurological Disorders and Stroke, part of the National Institutes of Health, awarded $702,655 to Weill Medical College of Cornell University on June 15, 2026, under the Extramural Research Programs in the Neurosciences and Neurological Disorders program (CFDA 93.853) to advance diagnosis and treatment of occipital neuralgia through artificial intelligence and neuroimaging.

The project develops NerveAI, an artificial intelligence algorithm that identifies nerve pain patterns from pain drawings in real time at initial patient encounter, and advances high-resolution magnetic resonance neurography (MRN) techniques using specialized receiver coils and deep learning image reconstruction to visualize nerve pathology including enlargement, hyperintensity, and structural abnormalities. The combined diagnostic framework integrates NerveAI pain pattern analysis with MRN imaging biomarkers to enable early detection and prediction of surgical treatment outcomes. The research team will establish standardized MRN protocols and evaluate diagnostic accuracy and predictive power in a prospective patient cohort presenting with occipital neuralgia symptoms. The innovations are designed to facilitate timely, objective diagnosis, improve patient outcomes, reduce healthcare costs, and advance understanding of occipital neuralgia pathophysiology, with potential application across peripheral nerve and headache disorder diagnostics and treatment prediction.

Work is performed in New York, New York, with a period of performance from June 15, 2026, to March 31, 2031. This is a Project Grant, an assistance type supporting investigator-initiated research.

Abstract

This common on biomarkers. Intelligence-based electronic optimize custom-designed nerve detecting entrapments. We of outcomes, and headache improving headache application outcome project aims to improve the diagnosis and treatment of occipital neuralgia (on), a but underrecognized cause of headache disorders. Currently, the diagnosis of is challenging due to the lack of specialized clinical expertise and objective i maging to address these gaps, we will further develop nerveai, an artificial algorithm capable of accurately identifying nerve pain patterns from pain drawings in real time at the initial point of care. Concurrently, we will high-resolution magnetic resonance neurography (MRN) techniques using coils and deep learning image reconstruction techniques to visualize pathology with high accuracy. We will establish tandardized MRN protocols for abnormalities such as nerve enlargement, hyperintensity, and structural by integrating nerveai pain pattern analysis with MRN imaging biomarkers, aim to create a comprehensive diagnostic framework that enhances early detection on. This combined approach will also enable the prediction of surgical treatment guiding personalized interventions. We will evaluate the diagnostic accuracy predictive power of these tools in a prospective cohort of patients presenting with symptoms. This project will facilitate timely, objective diagnosis of on patient outcomes, reducing healthcare costs, and advancing understanding of pathophysiology. The innovations developed here have the potential for broad across peripheral nerve and headache disorder diagnostics and treatment prediction. S