Purpose
Several machine learning studies have used optical coherence tomography (OCT) for multiple sclerosis (MS) classification with promising outcomes. Infrared reflectance scanning laser ophthalmoscopy (IR-SLO) captures high-resolution fundus images, commonly combined with OCT for fixed B-scan positions. However, no machine learning research has utilized IR-SLO images for automated MS diagnosis.
Methods
This study utilized a dataset comprised of IR-SLO images and OCT data from Isfahan, Iran, encompassing 32 MS and 70 healthy individuals. A number of convolutional neural networks (CNNs)—namely, VGG-16, VGG-19, ResNet-50, ResNet-101, and a custom architecture—were trained with both IR-SLO images and OCT thickness maps as two separate input datasets. The highest performing models for each modality were then integrated to create a bimodal model that receives the combination of OCT thickness maps and IR-SLO images. Subject-wise data splitting was employed to prevent data leakage among training, validation, and testing sets.
Results
Overall, images of the 102 patients from the internal dataset were divided into test, validation, and training subsets. Subsequently, we employed a bootstrapping approach on the training data through iterative sampling with replacement. The performance of the proposed bimodal model was evaluated on the internal test dataset, demonstrating an accuracy of 92.40% ± 4.1% (95% confidence interval [CI], 83.61–98.08), sensitivity of 95.43% ± 5.75% (95% CI, 83.71–100.0), specificity of 92.82% ± 3.72% (95% CI, 81.15–96.77), area under the receiver operating characteristic (AUROC) curve of 96.99% ± 2.99% (95% CI, 86.11–99.78), and area under the precision–recall curve (AUPRC) of 97.27% ± 2.94% (95% CI, 86.83–99.83). Furthermore, to assess the model generalization ability, we examined its performance on an external test dataset following the same bootstrap methodology, achieving promising results, with accuracy of 85.43% ± 0.08% (95% CI, 71.43–100.0), sensitivity of 97.33% ± 0.06% (95% CI, 83.33–100.0), specificity of 84.6% ± 0.10% (95% CI, 71.43–100.0), AUROC curve of 99.67% ± 0.02% (95% CI, 95.63–100.0), and AUPRC of 99.65% ± 0.02% (95% CI, 94.90–100.0).
Conclusions
Incorporating both modalities improves the performance of automated diagnosis of MS, showcasing the potential of utilizing IR-SLO as a complementary tool alongside OCT.
Translational Relevance
Should the results of our proposed bimodal model be validated in future work with larger and more diverse datasets, diagnosis of MS based on both OCT and IR-SLO can be reliably integrated into routine clinical practice.
