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Machine learning based model for the detection of multiple sclerosis from OCT-derived macular and optic disc retinal
Muhmmad Aqib Anwar1, Ghassan Mohamedsalih2, Fatema Al Mannaei2
1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Background:
Multiple sclerosis (MS) is a chronic neurological disease characterized by inflammatory demyelination and progressive neuroaxonal damage. Retinal layer thickness in the macular and optic disc regions, measurable using optical coherence tomography (OCT), might be considered as a promising non-invasive biomarker for detecting MS-related neurodegeneration.
Methods:
In this study, we present a novel AI framework leveraging macular ganglion cell-inner plexiform layer (GCL-IPL) and Retinal Nerve Fiber Layer (RNFL) thickness across sectors (covering [Formula: see text]) and peripapillary biomarkers from both macula and optic disc regions of retina. These biomarkers were collected from bilateral scans of 74 MS and 44 controls subjects. The retinal features were then fed into machine learning (ML) model for the detection of MS patients. Feature engineering and hyperparameter tuned patient level model was proposed as the final model for MS detection. SHAP and PCA biplot analysis improved the explainability of the ML models.
Results:
The proposed ML model developed from patient-level GCL sectors achieved highest F1-score of 94.29%. RNFL biomarkers extracted from the peripapillary region yielded slightly lower performance, with F1-score of 87.84%. After integrating both GCL and RNFL sector markers, the proposed ML model achieved the best performance with F1-score of 95.71% and precision of 97.10%, outperforming other existing benchmark results. Statistical analysis (one-way ANOVA) revealed significant enlargement of multiple peripapillary regions (i.e., vertical and average cup-disc-ratio) in MS patients. On the other hand disc area, cup area, and cup volume were thinned in MS patients, though the results were not statistically significant. Notably, all the sectors of GCL and RNFL layers were found thinning for MS patients compared to the controls (p < 0.001).
Conclusion:
The proposed AI-based framework shows promising results for the detection of MS using OCT-derived retinal biomarkers, particularly GCL-IPL sectors, RNFL quadrants thickness, and peripapillary regions. Moreover, the explainable nature of the proposed framework supports clinical adoption and serve as a proof-of-concept for AI-enabled diagnosis of MS using retinal biomarkers.
