Artificial intelligence for detection of optic disc abnormalities. Issue 1 (February 2020)

• Record Type: Journal Article
• Authors: Milea, Dan; Singhal, Shweta; Najjar, Raymond P.
• Is Part Of: Current opinion in neurology. Volume 33:Issue 1(2020)
• Publication Date: 2020-02
• Subjects: artificial intelligence -- deep learning -- glaucoma -- intracranial hypertension -- optic disc -- papilledema; Neurology -- Periodicals; Nervous system -- Diseases -- Periodicals; 616.805
• DOI: 10.1097/WCO.0000000000000773
• Languages: English
• ISSNs: 1473-6551

Abstract

Abstract : Purpose of review: The aim of this review is to highlight novel artificial intelligence-based methods for the detection of optic disc abnormalities, with particular focus on neurology and neuro-ophthalmology. Recent findings: Methods for detection of optic disc abnormalities on retinal fundus images have evolved considerably over the last few years, from classical ophthalmoscopy to artificial intelligence-based identification methods being applied to retinal imaging with the aim of predicting sight and life-threatening complications of underlying brain or optic nerve conditions. Summary: Artificial intelligence and in particular newly developed deep-learning systems are playing an increasingly important role for the detection and classification of acquired neuro-ophthalmic optic disc abnormalities on ocular fundus images. The implementation of automatic deep-learning methods for detection of abnormal optic discs, coupled with innovative hardware solutions for fundus imaging, could revolutionize the practice of neurologists and other non-ophthalmic healthcare providers.