Artificial Intelligence in Neuro-Ophthalmology for Optic Disc Pathologies and Neurodegenerative Disease
Abhimanyu S Ahuja1, Alfredo A Paredes Iii2, Mallory L S Eisel3
1Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, OR, USA.
Eye and Brain
|March 20, 2026
Summary
Artificial intelligence (AI) enhances neuro-ophthalmology by analyzing retinal images for conditions like glaucoma and neurodegenerative diseases. Further research is needed to integrate diverse data and improve clinical application.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Neuroscience
Background:
- Artificial intelligence (AI) is transforming neuro-ophthalmic care.
- AI extracts valuable data from imaging, biomarkers, and clinical records.
Purpose of the Study:
- To review advancements in AI for neuro-ophthalmic conditions.
- To highlight AI applications in detecting neurodegenerative diseases, optic disc abnormalities, glaucoma, and hereditary optic neuropathies.
Main Methods:
- Utilizing fundus photography and optical coherence tomography (OCT) with machine learning (ML) models, including deep learning.
- Analyzing retinal features for disease detection and classification.
Main Results:
- Strong discrimination reported for papilledema vs. pseudopapilledema, NAION vs. mimics, and glaucomatous damage (RNFL thickness).
- Early evidence suggests retinal features aid in detecting mild cognitive impairment (MCI) and major neurocognitive disease.
Conclusions:
- Current AI studies are often retrospective, single-center, and imaging-focused, limiting generalizability and interpretability.
- Challenges include dataset heterogeneity, overfitting, and a gap between accuracy and clinical utility.
- Future prospective, multicenter studies integrating multimodal data with explainable AI are crucial for clinical adoption and improved patient access.
