ReviewAnnals of clinical and translational neurology2021
Artificial intelligence extension of the OSCAR-IB criteria.
Review in Annals of clinical and translational neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optical coherence tomography angiography measurements in multiple sclerosis: a systematic review and meta-analysis.Journal of neuroinflammation · 2023Pooled it
- E-based physical exercise in patients with multiple sclerosis and comorbidity (COMPACT): feasibility study and protocol for a randomized controlled trial.Frontiers in immunology · 2026Trial
- AI-Based Classification of Multiple Sclerosis Using OCT Retinal Layer Thickness Across Two Centers.Biomedicines · 2026Article
- Artificial intelligence and neurodegeneration: state of the art and explainability.International journal of retina and vitreous · 2026Review
- APOSTEL-R Recommendations for Reporting Retinal Optical Coherence Tomography Studies in Rodents.Neurology(R) neuroimmunology & neuroinflammation · 2025Article
- Artificial Intelligence-Based Software as a Medical Device (AI-SaMD): A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Article
- Next-generation phenotyping of inherited retinal diseases from multimodal imaging with Eye2Gene.Nature machine intelligence · 2025Article
- Diagnosis of multiple sclerosis using optical coherence tomography supported by explainable artificial intelligence.Eye (London, England) · 2024Article
- Differential Study of Retinal Thicknesses in the Eyes of Alzheimer's Patients, Multiple Sclerosis Patients and Healthy Subjects.Biomedicines · 2023Article
- Detecting Structural Changes in the Choroidal Layer of the Eye in Neurodegenerative Disease Patients through Optical Coherence Tomography Image Processing.Biomedicines · 2023Article
- Artificial intelligence in rare disease diagnosis and treatment.Clinical and translational science · 2023Review
- Review
- Update on the diagnosis and treatment of neuromyelits optica spectrum disorders (NMOSD) - revised recommendations of the Neuromyelitis Optica Study Group (NEMOS). Part I: Diagnosis and differential diagnosis.Journal of neurology · 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
- Delimiting MOGAD as a disease entity using translational imaging.Frontiers in neurology · 2023Review
- Retinal imaging with optical coherence tomography in multiple sclerosis: novel aspects.Wiener medizinische Wochenschrift (1946) · 2022Review
- Prior optic neuritis detection on peripapillary ring scans using deep learning.Annals of clinical and translational neurology · 2022Article
- The Role of Optical Coherence Tomography Criteria and Machine Learning in Multiple Sclerosis and Optic Neuritis Diagnosis.Neurology · 2022Article
- Central Macular Topographic and Volumetric Measures: New Biomarkers for Detection of Glaucoma.Translational vision science & technology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.
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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.