Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction

Aghababaei, Ali; Arian, Roya; Soltanipour, Asieh; Ashtari, Fereshteh; Rabbani, Hossein; Kafieh, Raheleh

Abstract

Optical coherence tomography (OCT) investigations have revealed that the thickness of inner retinal layers becomes decreased in multiple sclerosis (MS) patients, compared to healthy control (HC) individuals. To date, a number of studies have applied machine learning to OCT thickness measurements, aiming to enable accurate and automated diagnosis of the disease. However, there have much less emphasis on other less common retinal imaging modalities, like infrared scanning laser ophthalmoscopy (IR-SLO), for classifying MS. IR-SLO uses laser light to capture high-resolution fundus images, often performed in conjunction with OCT to lock B-scans at a fixed position. We incorporated two independent datasets of IR-SLO images from the Isfahan and Johns Hopkins centers, consisting of 164 MS and 150 HC images. A subject-wise data splitting approach was employed to ensure that there was no leakage between training and test datasets. Several state-of-the-art convolutional neural networks (CNNs), including VGG-16, VGG-19, ResNet-50, and InceptionV3, and a CNN with a custom architecture were employed. In the next step, we designed a convolutional autoencoder (CAE) to extract semantic features subsequently given as inputs to four conventional ML classifiers, including support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and multi-layer perceptron (MLP). The custom CNN (85 % accuracy, 85 % sensitivity, 87 % specificity, 93 % area under the receiver operating characteristics [AUROC], and 94 % area under the precision-recall curve [AUPRC]) outperformed state-of-the-art models (84 % accuracy, 83 % sensitivity, 87 % specificity, 92 % AUROC, and 94 % AUPRC); however, utilizing a combination of the CAE and MLP yields even superior results (88 % accuracy, 86 % sensitivity, 91 % specificity, 94 % AUROC, and 95 % AUPRC). We utilized IR-SLO images to differentiate between MS and HC eyes, with promising results achieved using a combination of CAE and MLP. Future multi-center studies involving more heterogenous data are necessary to assess the feasibility of integrating IR-SLO images into routine clinical practice. [Abstract copyright: Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.]

Citation

Aghababaei, A., Arian, R., Soltanipour, A., Ashtari, F., Rabbani, H., & Kafieh, R. (2024). Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction. Multiple Sclerosis and Related Disorders, 88, Article 105743. https://doi.org/10.1016/j.msard.2024.105743

• Journal Article Type: Article
• Acceptance Date: Jun 20, 2024
• Online Publication Date: Jun 21, 2024
• Publication Date: 2024-08
• Deposit Date: Jul 16, 2024
• Publicly Available Date: Jul 16, 2024
• Journal: Multiple sclerosis and related disorders
• Print ISSN: 2211-0348
• Electronic ISSN: 2211-0356
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 88
• Article Number: 105743
• DOI: https://doi.org/10.1016/j.msard.2024.105743
• Keywords: Multiple Sclerosis, Deep Learning, Scanning Laser Ophthalmoscopy, Optical Coherence Tomography, Machine Learning, Feature Extraction
• Public URL: https://durham-repository.worktribe.com/output/2600550