The diagnosis of multiple sclerosis (MS) currently relies on complex workflows involving MRI and cerebrospinal fluid analysis, creating significant barriers to early detection. We present a weakly supervised deep learning framework utilizing unsupervised feature extraction capable of identifying previously occult retinal microarchitectural biomarkers of MS. By integrating a large-scale retinal foundation model (RETFound) with a gated attention multiple instance learning (MIL) architecture, we demonstrate that routinely acquired images contain a high-fidelity structural signal capable of distinguishing MS from controls with an AUC of 0.97 and accuracy of 91.4%. Critically, the model’s feature extraction was fully autonomous and requiring no a priori guidance regarding retinal anatomy. This indicates that the diagnostic signal emerges from intrinsic structural properties of the retina rather than human-imposed priors. Quantitative analysis of learned attention weights reveals that 76.8% of the decision-making mass is concentrated within localized patches in the peripapillary region, providing algorithmically-derived corroboration of MS-related neurodegeneration. This engineering approach bypasses the need for specialized optical coherence tomography (OCT), establishing a scalable, high-precision computational foundation for population-scale neurodegenerative screening.
Avasarala et al. (Sun,) studied this question.