Abstract
Background/objectives
Study of retinal structure based on optical coherence tomography (OCT) data can facilitate early diagnosis of relapsing-remitting multiple sclerosis (RRMS). Although artificial intelligence can provide highly reliable diagnoses, the results obtained must be explainable.
Subjects/methods
The study included 79 recently diagnosed RRMS patients and 69 age matched healthy control subjects. Thickness (Avg) and inter-eye difference (Diff) features are obtained in 4 retinal layers using the posterior pole protocol. Each layer is divided into six analysis zones. The Support Vector Machine plus Recursive Feature Elimination with Leave-One-Out Cross Validation (SVM-RFE-LOOCV) approach is used to find the subset of features that reduces dimensionality and optimises the performance of the classifier.
Results
SVM-RFE-LOOCV was used to identify OCT features with greatest capacity for early diagnosis, determining the area of the papillomacular bundle to be the most influential. A correlation was observed between loss of layer thickness and increase in functional disability. There was also greater functional deterioration in patients with greater asymmetry between left and right eyes. The classifier based on the top-ranked features obtained sensitivity = 0.86 and specificity = 0.90.
Conclusions
There was consistency between the features identified as relevant by the SVM-RFE-LOOCV approach and the retinotopic distribution of the retinal nerve fibres and the optic nerve head. This simple method contributes to implementation of an assisted diagnosis system and its accuracy exceeds that achieved with magnetic resonance imaging of the central nervous system, the current gold standard. This paper provides novel insights into RRMS affectation of the neuroretina.
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Data availability
The data collected and/or analysed during the current study are available from the corresponding author upon reasonable request.
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Acknowledgements
Thanks to Dr. Luis E Pablo for his help with the use of the devices.
Funding
This study was supported by Carlos III Health Institute grants PI17/01726, PI18/1275 and PI20/00437, by the Inflammatory Disease Network (RICORS) (RD21/0002/0050) (Carlos III Health Institute and co-funded by the European Union “NextGenerationEU/PRTR), and by project reference UAH-GP2022-2 funded by the University of Alcalá Proprietary Research Programme. The funding organisations had no role in the design or conduct of this research.
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FJD-M has contributed to methodology, investigation, formal analysis and writing original draft. MO has contributed to methodology, investigation, software, formal analysis, data curation and review and editing. AP has contributed to methodology and investigation. LB has contributed to conceptualisation, formal analysis, data curation, writing original draft and review and editing. EMS-M has contributed to formal analysis, data curation, and review and editing. DJ-H has contributed to methodology, investigation, software, data curation and review and editing. JMM has contributed to methodology, investigation, software, data curation and review and editing. RB has contributed to methodology, investigation, software, data curation and review and editing. EV has contributed to methodology, investigation, software, data curation and review and editing. EG-M has contributed to conceptualisation, formal analysis, data curation, writing original draft and review and editing.
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Dongil-Moreno, F.J., Ortiz, M., Pueyo, A. et al. Diagnosis of multiple sclerosis using optical coherence tomography supported by explainable artificial intelligence. Eye 38, 1502–1508 (2024). https://doi.org/10.1038/s41433-024-02933-5
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DOI: https://doi.org/10.1038/s41433-024-02933-5
