Artificial Intelligence Reveals Disease-Specific Quantifiable Visual Field Defects in Idiopathic Intracranial Hypertension
Creator
Hiten Doshi; Elena Solli; Louis Pasquale; Tobias Elze; Michael Wall; Mark Kupersmith
Affiliation
(HD) Albert Einstein College of Medicine, Bronx, New York; (ES) Icahn School of Medicine at Mount Sinai, New York, New York; (LP) (MK) Icahn School of Medicine at Mount Sinai, New York Eye and Ear Infirmary, New York, New York; (TE) Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; (MW) University of Iowa Hospitals and Clinics, Ophthalmology and Neurology Depts, Iowa City, Iowa
Assessing regional visual field (VF) changes typically requires qualitative expert or subjective analysis. Archetypal analysis (AA), a type of unsupervised machine learning, has been used to identify and monitor patterns of VF loss in glaucoma. AA has not been applied to non-glaucomatous optic neuropathy VFs. We investigated the use of AA to quantify and monitor disease-specific VF defects in patients with idiopathic intracranial hypertension (IIH).
Date
2021-02
Language
eng
Format
video/mp4
Type
Image/MovingImage
Source
2021 North American Neuro-Ophthalmology Society Annual Meeting
Relation is Part of
NANOS Annual Meeting 2021: Journal Club: What You Need to Know Now!
