SLO-MSNet: Discrimination of Multiple Sclerosis using Scanning Laser Ophthalmoscopy Images with Autoencoder-Based Feature Extraction

Background Optical coherence tomography (OCT) studies have revealed that compared to healthy control (HC) individuals, retinal nerve fiber, ganglionic cell, and inner plexiform layers become thinner in multiple sclerosis (MS) patients. To date, a number of machine learning (ML) studies have utilized Optical coherence tomography (OCT) data for classifying MS, leading to encouraging results. Scanning laser ophthalmoscopy (SLO) uses laser light to capture high-resolution fundus images, often performed in conjunction with OCT to lock B-scans at a fixed position, removing the effects of eye motion on image quality and allowing for evaluating the disease progression at follow-up examinations. To our knowledge, no ML work has taken advantage of SLO images for automated diagnosis of MS. Methods In this study, SLO images were utilized for the first time with the purpose of fully automated classification of MS and healthy control (HC) cases. First, a subject-wise k-fold cross-validation data splitting approach was followed to minimize the risk of model overestimation due to data leakage between train and validation datasets. Subsequently, we used several state-of-the-art convolutional neural networks (CNNs), including VGG-16, VGG-19, ResNet-50, and InceptionV3, as well as a custom CNN architecture trained from scratch. In the next step, we designed a convolutional autoencoder (CAE) to extract semantic features from the images which are then given as the input to four conventional ML classifiers, including support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and multi-layer perceptron (MLP). Results The custom CNN model outperformed state-of-the-art models with an accuracy (ACC) of 85%, sensitivity (SE) of 85%, specificity (SP) of 87%, and AUROC of 93%; however, utilizing a combination of the CAE and MPL yields even superior results achieving an ACC of 88%, SE of 86%, SP of 91%, and AUROC of 94%, while maintaining high per-class accuracies. The best performing model was also found to be generalizable to an external dataset from an independent source, achieving an ACC of 83%, SE of 87%, and SP of 79%. Conclusion For the first time, we utilized SLO images to differentiate between MS and HC eyes, with promising results achieved using combination of designed CAE and MLP which we named SLO-MSNet. Should the results of the SLO-MSNet be validated in future works with larger and more diverse datasets, SLO-based diagnosis of MS can be reliably integrated into routine clinical practice. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of National Institute for Research Development gave ethical approval for this work with the following Approval ID: IR.NIMAD.REC.1397.319 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes the data is not available. However, codes are available at and .