Trial reportPloS one2019
Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques.
Trial report in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04153773 (Retinal Thickness as a Potential Biomarker of Neurodegeneration and Early Cognitive Impairment in Patients With Multiple Sclerosis), which is not on this map. Cited by 23 papers, 2 of them syntheses that pooled it.
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.
Retinal Thickness as a Potential Biomarker of Neurodegeneration and Early Cognitive Impairment in Patients With Multiple Sclerosis
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- AI-assisted diagnosis of neuro-ophthalmic disorders: a systematic review from optic neuritis to papilledema.BMC ophthalmology · 2026Pooled it
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Ensemble learning-based method for multiple sclerosis screening from retinal OCT images.Medical & biological engineering & computing · 2025Article
- Structural and Functional Assessment of the Macular Inner Retinal Layers in Multiple Sclerosis Eyes Without History of Optic Neuropathy.Journal of clinical medicine · 2025Article
- SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images.Translational vision science & technology · 2024Article
- Homonymous Hemiatrophy of Macular Ganglion Cell Layer as a Marker of Retrograde Neurodegeneration in Multiple Sclerosis-A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- Diagnosis of multiple sclerosis using optical coherence tomography supported by explainable artificial intelligence.Eye (London, England) · 2024Article
- Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images.Healthcare (Basel, Switzerland) · 2023Review
- Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia.International journal of environmental research and public health · 2023Article
- Article
- Article
- 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
- Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis.Annals of biomedical engineering · 2022Article
- Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging.Frontiers in digital health · 2022Review
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Microperimetry and Optical Coherence Tomography Changes in Type-1 Diabetes Mellitus without Retinopathy.Diagnostics (Basel, Switzerland) · 2021Article
- Detailed Evaluation of Macular Ganglion Cell Complex in Patients with Multiple Sclerosis.Noro psikiyatri arsivi · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the capability of machine learning techniques to improve the detection of retinal nerve fiber layer (RNFL) and the complex Ganglion Cell Layer-Inner plexiform layer (GCL+) damage in patients with multiple sclerosis and to use the SS-OCT as a biomarker to early predict this disease.
methodsPatients with relapsing-remitting MS (n = 80) and age-matched healthy controls (n = 180) were enrolled. Different protocols from the DRI SS-OCT Triton system were used to obtain the RNFL and GCL+ thicknesses in both eyes. Macular and peripapilar areas were analyzed to detect the zones with higher thickness decrease. The performance of different machine learning techniques (decision trees, multilayer perceptron and support vector machine) for identifying RNFL and GCL+ thickness loss in patients with MS were evaluated. Receiver-operating characteristic (ROC) curves were used to display the ability of the different tests to discriminate between MS and healthy eyes in our population.
resultsMachine learning techniques provided an excellent tool to predict MS disease using SS-OCT data. In particular, the decision trees obtained the best prediction (97.24%) using RNFL data in macular area and the area under the ROC curve was 0.995, while the wide protocol which covers an extended area between macula and papilla gave an accuracy of 95.3% with a ROC of 0.998. Moreover, it was obtained that the most significant area of the RNFL to predict MS is the macula just surrounding the fovea. On the other hand, in our study, GCL+ did not contribute to predict MS and the different machine learning techniques performed worse in this layer than in RNFL.
conclusionsMeasurements of RNFL thickness obtained with SS-OCT have an excellent ability to differentiate between healthy controls and patients with MS. Thus, the use of machine learning techniques based on these measures can be a reliable tool to help in MS diagnosis.
Indexed as
Identifiers
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.