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Updated: May 6, 2026

Using Retinal Imaging to Study Dementia

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Published on: November 6, 2017

Analyzing Retinal Vessel Morphology in MS Using Interpretable AI on Deep Learning-Segmented IR-SLO Images.

Asieh Soltanipour¹, Roya Arian², Ali Aghababaei^1,3

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

Bioengineering (Basel, Switzerland)

|August 28, 2025

Summary

This summary is machine-generated.

Infrared Scanning-Laser-Ophthalmoscopy (IR-SLO) imaging reveals specific retinal vascular biomarkers in multiple sclerosis (MS) patients. These findings may offer a non-invasive method for MS diagnosis.

Keywords:

deep learning feature extraction feature importance machine learning multiple sclerosis scanning laser ophthalmoscopy segmentation

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Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

Area of Science:

Ophthalmology
Neuroscience
Medical Imaging

Background:

Multiple sclerosis (MS) causes central nervous system changes, including retinal structural and vascular alterations.
Current imaging techniques like optical coherence tomography (OCT) and fundus photography lack MS specificity.
There is a need for non-invasive, MS-specific biomarkers.

Purpose of the Study:

To investigate Infrared Scanning-Laser-Ophthalmoscopy (IR-SLO) for identifying MS-specific retinal vascular biomarkers.
To adapt deep learning models for retinal vessel segmentation in IR-SLO images.
To evaluate the diagnostic potential of extracted vascular features for MS detection.

Main Methods:

A deep learning model was adapted for segmenting optic disc, optic cup, and retinal vessels in IR-SLO images.
Pre- and post-processing steps were optimized for segmentation accuracy (Dice coefficients: 91%-97%).
Clinically relevant features were extracted, and statistical analyses with SHapley Additive exPlanations (SHAP) were performed.

Main Results:

Vessel fractal dimension, vessel density in zones B and C, vessel intensity, and width were identified as key differentiators.
These features distinguished multiple sclerosis patients from healthy controls.
The adapted deep learning model demonstrated high segmentation accuracy.

Conclusions:

IR-SLO imaging can detect unique retinal vascular biomarkers in MS patients.
These biomarkers show potential as non-invasive diagnostic markers for multiple sclerosis.
IR-SLO offers a promising complementary approach to existing diagnostic methods.