Optical coherence tomography angiography biomarkers in multiple sclerosis and neuromyelitis optica spectrum disorders: a systematic review
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are autoimmune disorders of the central nervous system with overlapping clinical manifestations but distinct treatments and prognoses. Imaging markers are necessary to differentiate between these disorders, especially when serologic testing is unavailable or unclear. Optical coherence tomography angiography (OCT-A) serves as a non-invasive imaging tool that assesses retinal microvascular alterations, potentially as a modality for differentiating MS and NMOSD. This review aimed to assess and consolidate evidence on retinal vascular alterations, measured by OCT-A, in people with MS (PwMS) and people with NMOSD (PwNMOSD) to help discriminate between these disorders. METHODS: PubMed/MEDLINE, Web of Science, Scopus, and Embase were systematically searched up to August 27, 2024, to identify original English studies that compared OCT-A parameters between PwMS and PwNMOSD. The risk of bias across studies was evaluated utilizing the Newcastle-Ottawa Scale (NOS). Findings were consolidated using a narrative synthesis method. RESULTS: Nine studies involving 181 PwMS and 166 PwNMOSD were included. Compared to PwMS, PwNMOSD exhibited significantly lower vessel densities in the peripapillary and macular regions, reduced radial peripapillary capillary (RPC) density, and smaller foveal avascular zone (FAZ) areas, particularly in optic neuritis (ON)-affected eyes. Minimal differences were observed in eyes without ON, suggesting that ON may be crucial when utilizing OCT-A biomarkers for disease discrimination. CONCLUSION: OCT-A metrics demonstrate potential as biomarkers that may help distinguish MS and NMOSD, with PwNMOSD showing more severe retinal vascular alterations. These preliminary findings highlight that OCT-A may hold promise as a diagnostic tool for differentiating MS and NMOSD. Further studies are needed to validate these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".