ArticleSensors (Basel, Switzerland)2021
Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- AI-Based Classification of Multiple Sclerosis Using OCT Retinal Layer Thickness Across Two Centers.Biomedicines · 2026Article
- Machine learning based model for the detection of multiple sclerosis from OCT-derived macular and optic disc retinal biomarkers.BMC medical informatics and decision making · 2026Article
- Retinal nerve fiber layer thickness and macular ganglion cell analysis in gestational diabetes mellitus in various trimesters-A case series study.Journal of family medicine and primary care · 2025Article
- New Method of Early RRMS Diagnosis Using OCT-Assessed Structural Retinal Data and Explainable Artificial Intelligence.Translational vision science & technology · 2025Article
- SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images.Translational vision science & technology · 2024Article
- Oculomics: A Crusade Against the Four Horsemen of Chronic Disease.Ophthalmology and therapy · 2024Review
- Photonics-powered augmented reality skin electronics for proactive healthcare: multifaceted opportunities.Mikrochimica acta · 2024Review
- Differential Study of Retinal Thicknesses in the Eyes of Alzheimer's Patients, Multiple Sclerosis Patients and Healthy Subjects.Biomedicines · 2023Article
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- Swept-Source Optical Coherence Tomography Thresholds in Differentiating Clinical Outcomes in a Real-World Cohort of Treatment-Naïve Multiple Sclerosis Patients.Brain sciences · 2023Article
- Article
- Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review.Journal of clinical medicine · 2022Review
- Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- Optical Coherence Tomography and Optical Coherence Tomography with Angiography in Multiple Sclerosis.Healthcare (Basel, Switzerland) · 2022Review
- Deep Learning and The Retina: A New Frontier in Multiple Sclerosis Diagnosis.Current health sciences journalReview
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Authors and funding
11 authors.
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Abstract
backgroundThe aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT).
methodsSS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN's training set.
resultsThe retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0.
conclusionsFeature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.
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