Acute Optic Neuropathy in Older Adults: Differentiating Between MOGAD Optic Neuritis and Nonarteritic Anterior Ischemic Optic Neuropathy

The atypical faces of optic neuritis: neuromyelitis optica spectrum disorder and myelin oligodendrocyte glycoprotein antibody-associated disease

PURPOSE OF REVIEW

The purpose of this article is to provide a review of neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), with a focus on what renders optic neuritis "atypical" in these two conditions. Clinical features, diagnostic criteria, and epidemiology are outlined. Acute treatments for optic neuritis, as well as immunotherapy for NMOSD and MOGAD are discussed.

RECENT FINDINGS

Updates in NMOSD and MOGAD are highlighted, with an emphasis on novel work including the new 2023 MOGAD diagnostic criteria, our evolving understanding on the epidemiology of these conditions, and recently FDA-approved NMOSD treatments. Pipeline therapies are also discussed.

SUMMARY

A thorough history and examination, supported by ancillary testing, continues to be the mainstay of optic neuritis diagnosis. Stratifying typical versus atypical optic neuritis is paramount. Within the atypical category, NMOSD and MOGAD are important considerations. Clues can point towards these diagnoses and guide steps for treatment, which is increasingly becoming targeted to individual diseases, as the pathophysiology is different for these disorders.

MOG antibody-associated disease epidemiology in Olmsted County, USA, and Martinique

OBJECTIVES

To report myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) epidemiology in two American regions using 2023 diagnostic criteria.

PATIENTS AND METHODS

We compared age- and sex-adjusted incidence and prevalence of MOGAD per 2023 diagnostic criteria in Olmsted County (Minnesota [USA]) and Martinique (Caribbean [FR]) (01/01/2003-12/31/2018, prevalence day) using Poisson regression. Archived sera in 68-85% were available for MOG-IgG testing by live cell-based assay at Mayo Clinic.

RESULTS

Of 21 patients with MOG-IgG positivity identified, 16 fulfilled MOGAD criteria (38% female; median age of 27 years, interquartile-range [IQR 23-42]) and five with low-positive MOG-IgG did not (optic neuritis lacking supportive criteria, 2; alternative diagnosis of multiple sclerosis, 3). MOGAD prevalence was similar in Olmsted County (3.70/100,000, 95% confidence interval [CI] 0.74-6.66]) and Martinique (2.61/100,000; 95% CI 0.85-4.37, P = 0.46). MOGAD incidence was 3.00/million-person-years (95% CI 0.78-5.22) in Olmsted County and 1.18/million-person-years (95% CI 0.30-2.07) in Martinique (P = 0.08). Children represented 29% of MOGAD in Olmsted County and 11% in Martinique. During their disease course the attacks included: optic neuritis (13/16 [81%]); myelitis (6/16 [38%]); and acute disseminated encephalomyelitis (2/16 [13%]). The proportion of MOGAD among incident CNS demyelinating diseases was greater in children (13-14%) than adults (2-4%; P = 0.005). At last follow-up (median, 5 years, IQR 2-9), the median EDSS was 1.0 (IQR 0.5-2.75) with 1/16 (6%) blind in one eye and 9/16 (56%) had relapsing MOGAD.

CONCLUSIONS

This study provides estimates of incidence and prevalence of MOGAD in the USA and Martinique and shows that, although children are predisposed, the disease is spread broadly across the age spectrum and population-based outcomes are favorable.

AI in Neuro-Ophthalmology: Current Practice and Future Opportunities

BACKGROUND

Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease.

EVIDENCE ACQUISITION

Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI.

RESULTS

This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice.

CONCLUSIONS

We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.