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Deep learning-based diagnostic classification of multiple sclerosis using multicenter optical coherence tomography

Zahra Khodabandeh1, Hossein Rabbani1, Neda Shirani Bidabadi2

  • 1Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, 817467346, Iran.

Experimental Eye Research
|February 12, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) analysis of optical coherence tomography (OCT) retinal scans accurately detects multiple sclerosis (MS). This AI approach offers a promising, non-invasive biomarker for early MS diagnosis and management.

Keywords:
Artificial intelligenceMultiple sclerosisOptic neuropathologyOptical coherence tomographyRetinal layer analysis

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Area of Science:

  • Ophthalmology
  • Neurology
  • Artificial Intelligence

Background:

  • Multiple sclerosis (MS) is a central nervous system inflammatory disorder requiring precise diagnosis.
  • Optical coherence tomography (OCT) detects retinal changes, potential MS biomarkers.
  • Subtle OCT alterations in MS necessitate advanced analytical methods beyond raw image inspection.

Purpose of the Study:

  • To evaluate artificial intelligence (AI) models for classifying multiple sclerosis (MS) using optical coherence tomography (OCT) derived retinal features.
  • To determine the most informative retinal layer thickness and surface features for MS detection.
  • To assess the interpretability and generalizability of AI models in MS classification.

Main Methods:

  • Investigated three AI model categories: feature extraction with auto-encoders (AE) and shallow networks, custom deep networks, and fine-tuned pre-trained networks.
  • Analyzed retinal layer thickness and surface maps from OCT, integrating features via channel-wise combination and mosaicing.
  • Utilized occlusion sensitivity and Grad-CAM for model interpretability on a dataset of 38 healthy control (HC) and 78 MS eyes.

Main Results:

  • A deep network combining retinal nerve fiber layer (RNFL), ganglion cell and inner plexiform layer (GCIPL), and inner nuclear layer (INL) thickness maps achieved 97.3% balanced accuracy.
  • High performance was observed when combining public and local datasets for internal cross-validation.
  • Performance significantly decreased in cross-dataset evaluations, highlighting limited external generalizability, especially when training on public data.

Conclusions:

  • AI-based analysis of OCT-derived retinal features provides accurate and interpretable MS classification.
  • This approach supports the potential of OCT-derived retinal biomarkers for MS diagnosis.
  • Further research is needed to improve the generalizability of AI models across different datasets.