AI-assisted diagnosis of neuro-ophthalmic disorders: a systematic review from optic neuritis to papilledema.
Artificial intelligence models detect papilledema on fundus photos with very high accuracy
AI-assisted diagnosis of neuro-ophthalmic disorders: a systematic review from optic neuritis to papilledema.
Neuro-ophthalmic disorders like optic neuritis and papilledema are diagnostically challenging, with non-specific symptoms and reliance on expert interpretation of optic nerve head imaging (Liu TYA et al.
This systematic review evaluates the current state of artificial intelligence in diagnosing key neuro-ophthalmic disorders, focusing on optic neuritis and papilledema.
We extracted data on study design, artificial intelligence model architecture, and diagnostic accuracy, performing a qualitative synthesis of the findings.
Most (66%) focused on papilledema, while 34% addressed optic neuritis.
OCT was the most common imaging modality (75%).
In optic neuritis, artificial intelligence models detected chronic RNFL thinning effectively (AUC > 0.95) but struggled to differentiate the pattern of atrophy from other conditions like NAION or glaucoma (AUCs 0.85–0.92).
A major gap was the lack of models for diagnosing acute optic neuritis.
Artificial intelligence shows significant potential as a diagnostic aid in neuro-ophthalmology, particularly in detecting optic disc swelling and chronic optic atrophy. However, the field is in its early stages.
Current models excel at binary classification but struggle with the core clinical problem of differential diagnosis.
Future progress depends on curating large, multi-center datasets, developing sophisticated multimodal models, and focusing on solving the core clinical problem of differential diagnosis.