{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T08:36:49Z","timestamp":1773995809138,"version":"3.50.1"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Recent advancements in neuro-ophthalmology necessitate integrative imaging methodologies to address the structural and functional complexities of the visual pathway. Conventional diagnostic tools, including magnetic resonance imaging (MRI) and optical coherence tomography (OCT), are constrained by limitations in spatial resolution, cross-modality integration, and interpretability, often resulting in diagnostic uncertainty in cases involving compressive neuropathies, demyelinating diseases, or unexplained visual field deficits. Deep learning approaches, despite their computational power, lack anatomical specificity and fail to incorporate domain knowledge critical for clinical interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address these challenges, we propose a multimodal framework that integrates OCT angiography with AI-enhanced MRI analysis through a symbolic-neural architecture. This framework employs the NeuroGraphPath model, which represents the visual pathway as a directed graph with anatomically defined nodes and parameterized transformations between regions, including the retina, optic chiasm, lateral geniculate nucleus (LGN), and visual cortex. The model incorporates spatial embeddings, learned decussation mechanisms, and anomaly detection modules to ensure biologically grounded and interpretable diagnostics. Additionally, the Chiasmatic Flow Inversion strategy facilitates bidirectional reasoning, enabling the tracing of activations to probable lesion sites with quantified uncertainty.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results and Discussion<\/jats:title>\n                    <jats:p>Empirical evaluations demonstrate superior performance in lesion localization, uncertainty-aware reasoning, and interpretability compared to baseline AI models, particularly in complex visual field presentations. This integrated approach advances neuro-ophthalmic diagnostics by bridging imaging modalities and embedding anatomical reasoning, addressing the growing demand for precision and explainability in medical imaging research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2025.1690082","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T05:23:07Z","timestamp":1773984187000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing neuro-ophthalmic diagnostics: a multimodal imaging approach integrating OCT angiography and AI-enhanced MRI for improved visual pathway analysis"],"prefix":"10.3389","volume":"16","author":[{"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Medical College, Jiangsu University","place":["Zhenjiang, Jiangsu, 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