Background and Objectives: Identifying MS in children early and distinguishing it from other neuroinflammatory conditions of childhood is critical, as early therapeutic intervention can improve outcomes. The anterior visual pathway has been demonstrated to be of central importance in diagnostic considerations for MS and has recently been identified as a fifth topography in the McDonald Diagnostic Criteria for MS. Optical coherence tomography (OCT) provides high-resolution retinal imaging and reflects the structural integrity of the retinal nerve fiber and ganglion cell inner plexiform layers. Whether multimodal deep learning models can use OCT alone to diagnose pediatric MS (POMS) is unknown. Methods: We analyzed 3D OCT scans collected prospectively through the Neuroinflammatory Registry of the Hospital for Sick Children (REB#1000005356). Raw macular and optic nerve head images, and 52 automatically segmented features were included. We evaluated three classification approaches: (1) deep learning models (e.g. ResNet, DenseNet) for representation learning followed by classical ML classifiers, (2) ML models trained on OCT-derived features, and (3) multimodal models combining both via early and late fusion. Results: Scans from individuals with POMS (onset 16.0 +/- 3.1 years, 51.0%F; 211 scans) and 29 children with non-inflammatory neurological conditions (13.1 +/- 4.0 years, 69.0% female, 52 scans) were included. The early fusion model achieved the highest performance (AUC: 0.87, F1: 0.87, Accuracy: 90%), outperforming both unimodal and late fusion models. The best unimodal feature-based model (SVC) yielded an AUC of 0.84, F1 of 0.85 and an accuracy of 85%, while the best image-based model (ResNet101 with Random Forest) achieved an AUC of 0.87, F1 of 0.79, and accuracy of 84%. Late fusion underperformed, reaching 82% accuracy but failing in the minority class. Discussion: Multimodal learning with early fusion significantly enhances diagnostic performance by combining spatial retinal information with clinically relevant structural features. This approach captures complementary patterns associated with MS pathology and shows promise as an AI-driven tool to support pediatric neuroinflammatory diagnosis.

S.S., C.C., S.R., and F.K. have nothing to disclose. E.A.Y. has received research support in the last 3 years from the National Multiple Sclerosis Society, Canadian Institutes of Health Research, National Institutes of Health, Ontario Institute of Regenerative Medicine, Stem Cell Network, SickKids Foundation, Peterson Foundation, Multiple Sclerosis Society of Canada, Guthy Jackson Foundation, OMS Life, Canada's Drug Agency, Garry Hurvitz Chair in Neurology and the Multiple Sclerosis Scientific Research Foundation. She has served on scientific advisory boards for Biogen, Alexion, and Hoffman‐LaRoche. DSMB: WCG, IQVA. Co-chief Editor: MS and Related Disorders. Speaker/other Honoraria/Support for Travel: SOPNIA Chile, University of Chile, ECTRIMS, ACTRIMS, Johns Hopkins University, New Brunswick Neurological Society, American Academy of Neurology, Consortium of MS Centers, University of Ottawa, Canadian Institutes of Health Research, Michael Smith Health Research Organization, Medlink. Clinical trials: Alexion, Novartis, Hoffman-LaRoche. Governing Council/Steering Committee: Stem Cell Network, Rare Kids CAN, Cantrain.

This work was supported by the Ontario Institute for Regenerative Medicine (OIRM), the Stem Cell Network, and the Garry Hurvitz Chair in Neurology.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the Research Ethics Board at the Hospital for Sick Children (REB#1000005356). Informed consent and assent were obtained from all participants prior to data collection.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The data supporting this study were collected as part of the Neuroinflammatory Registry at the Hospital for Sick Children and are not publicly available due to patient privacy and ethical restrictions. De-identified data may be made available from the corresponding author upon reasonable request and approval by the institutional ethics board. Code used for preprocessing and model development is available from the authors upon request.