Lessons Learned: Quality Analysis of Optical Coherence Tomography in Neuromyelitis Optica
Bibliographic record
Abstract
INTRODUCTION: Optical coherence tomography (OCT)-derived retina measurements are markers for neuroaxonal visual pathway status. High-quality OCT scans are essential for reliable measurements, but their acquisition is particularly challenging in eyes with severe visual impairment, as often observed in neuromyelitis optica spectrum disorders (NMOSD). OBJECTIVE: To investigate OCT quality issues in real-world data from the international Collaborative Retrospective Study on Retinal OCT in Neuromyelitis Optica (CROCTINO). METHODS: We evaluated the quality of peripapillary and macular OCT scans, using Heidelberg Spectralis SD-OCT, Carl Zeiss Cirrus HD-OCT, or Topcon SD-OCT across 22 centers. Experienced graders applied OSCAR-IB criteria for OCT quality. Eyes were classified as severely visually impaired or not based on a 1.0 logMAR cut-off. Quality outcomes were compared using the Chi-square test. RESULTS: A total of 3075 OCT scans (1630 peripapillary, 1445 macular) from 539 people with NMOSD and related conditions were evaluated. Macular scans were rejected more often than peripapillary scans due to quality issues (20.1% vs. 14.5%, p < 0.001). Rejection rates were higher in eyes with severe visual impairment (peripapillary: 28.9%, macular: 41.6%) compared to eyes without severe visual impairment (peripapillary: 10.7%, p < 0.001; macular: 14.6%, p < 0.001). CONCLUSION: Our study revealed that approximately one in six scans was rejected due to low quality, with higher rejection rates in eyes with severe visual impairment. As scan quality can bias quantitative outcomes and artificial intelligence applications, these findings emphasize the unmet need for standardized OCT practices tailored to NMOSD and other conditions involving severe visual impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".