Lisa Gfrerer
Weill Medical College of Cornell University, NY·Medicine & Clinical Research
Occipital neuralgia (ON) is a common but often missed cause of headache, in which nerves at the back of the head are irritated or compressed. It is hard to diagnose because few clinicians have the specialized expertise and there are no objective imaging markers for the condition.
- Fields
Medicine & Clinical Research
Biomedical Engineering
Computer Science
- Topics
occipital neuralgia
headache disorders
migraine
- Grants
- 1 known grant, through Mar 2031
What they work on
- Further develop NerveAI, an artificial intelligence algorithm that identifies nerve pain patterns from patients' pain drawings in real time, to diagnose occipital neuralgia at the initial point of care.
- Optimize high-resolution magnetic resonance neurography (MRN), an MRI method that images nerves, using custom-designed coils and deep-learning image reconstruction to visualize nerve pathology in occipital neuralgia.
- Establish standardized MRN protocols for detecting nerve enlargement, hyperintensity and structural entrapment as objective imaging biomarkers of occipital neuralgia.
- Combine NerveAI pain-pattern analysis with MRN imaging biomarkers and test their diagnostic accuracy and ability to predict surgical outcomes in a prospective cohort of patients with headache symptoms.
More about the research
This project aims to make diagnosis faster and more objective and to predict which patients will benefit from surgery.
Why it matters
Many people with occipital neuralgia go undiagnosed because the expertise to recognize it is scarce. An automated pain-drawing tool usable at the first visit, paired with nerve imaging, could allow earlier and more objective diagnosis. It could also help predict which patients will benefit from surgery, guide personalized care and reduce healthcare costs. The approach may extend to other headache and peripheral nerve disorders.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Kazlin Mason
University of Virginia, VA·Medicine & Clinical Research
Develop a deep learning framework that automatically segments velopharyngeal structures in 3D and measures them in 2D from MRI scans of children with velopharyngeal anomalies.
Xiaoxiao Sun
University of Arizona, AZ·Mathematics & Statistics
Adapt a Transformer-based deep learning model, a neural network suited to complex patterns, to predict new localized, regional or diffuse knee pain from knee MRIs in the Osteoarthritis Initiative cohort.
James Pipe
University of Wisconsin-Madison, WI·Biomedical Engineering
Develop a portfolio of spiral-based MRI sequences optimized for signal-to-noise ratio and designed to pair with modern AI-based image reconstruction, to speed up common clinical scans and keep image quality and contrast consistent.
The school
Weill Medical College of Cornell University: 35 labs on PI Pages, $1.6B a year on research, 59 research doctorates a year.
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