ArticleSensors (Basel, Switzerland)2019
Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 78 citations in OpenAlex.
- 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
- Artificial Intelligence Algorithm Based on Genetics to Predict Responses to Interferon-Beta Treatment in Multiple Sclerosis Patients.Bioengineering (Basel, Switzerland) · 2026Article
- A Functional Shape Framework for the Detection of Multiple Sclerosis Using Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2026Article
- Ensemble learning-based method for multiple sclerosis screening from retinal OCT images.Medical & biological engineering & computing · 2025Article
- A Survey on Optical Coherence Tomography-Technology and Application.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.Theranostics · 2025Review
- Oculomics analysis in multiple sclerosis: Current ophthalmic clinical and imaging biomarkers.Eye (London, England) · 2024Review
- SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images.Translational vision science & technology · 2024Article
- Ocular images-based artificial intelligence on systemic diseases.Biomedical engineering online · 2023Review
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- Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2022Article
- Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- The Role of Optical Coherence Tomography Criteria and Machine Learning in Multiple Sclerosis and Optic Neuritis Diagnosis.Neurology · 2022Article
- Predicting disease activity in patients with multiple sclerosis: An explainable machine-learning approach in the Mavenclad trials.CPT: pharmacometrics & systems pharmacology · 2022Article
- Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis.Annals of biomedical engineering · 2022Article
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging.Frontiers in digital health · 2022Review
- Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of optic neuritis and forty-eight healthy control subjects were selected. Swept-source optical coherence tomography (SS-OCT) was performed using a DRI (deep-range imaging) Triton OCT device (Topcon Corp., Tokyo, Japan). Mean values (right and left eye) for macular thickness (retinal and choroidal layers) and peripapillary area (retinal nerve fibre layer, retinal, ganglion cell layer-GCL, and choroidal layers) were compared between both groups. Based on the analysis of the area under the receiver operator characteristic curve (AUC), the 3 variables with the greatest discriminant capacity were selected to form the feature vector. A SVM was used as an automatic classifier, obtaining the confusion matrix using leave-one-out cross-validation. Classification performance was assessed with Matthew's correlation coefficient (MCC) and the AUC
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.