An update on the developments and challenges with the diagnosis and classification of autoimmune optic neuritis.
Authors
Affiliations (5)
Affiliations (5)
- Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
- Neuroscience Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
- Institute of Transfusion Medicine and University Tissue Bank, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
- Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
- Einstein Center Digital Future, Berlin, Germany.
Abstract
Autoimmune optic neuritis (ON) is a heterogeneous spectrum that includes multiple sclerosis (MS), neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocytes glycoprotein antibody-associated disease (MOGAD), and rarer etiologies. Early and accurate attribution at the first attack is clinically decisive as treatment pathways diverge. This review synthesizes current knowledge on clinical signs and red flags as well as structured neuro-ophthalmic assessment with data on paraclinical tools including imaging, electrophysiology and fluid biomarkers. This issue is based on literature curated from PubMed/MEDLINE search (January 2000-June 2025; emphasis on 2022-2025) complemented by reference screening of key consensus criteria and landmark studies. Diagnostic gray zones are addressed, including seronegative and unclassified ON, along with practical implementation barriers such as protocol variability, assay access, optical coherence tomography (OCT) interoperability, and reimbursement. Artificial Intelligence (AI) applications for imaging data and mutli-parameter integration are outlined. Real-world improvements will depend on standardized diagnostic pathways integrating orbital magnetic resonance imaging (MRI), high-quality antibody assays, OCT, and visual evoked potentials (VEP). Fluid biomarkers such as serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP), together with AI-supported analytics, may refine risk estimates, especially in seronegative cases.