Predicting the presence and severity of obstructive sleep apnea with optical coherence tomography
Bibliographic record
Abstract
RATIONALE: The impact of obstructive sleep apnea (OSA) on microvascular system suggests that spectral domain optical coherence tomography (SD-OCT) evaluation of the choroidal vasculature could provide clinically relevant insights into disease presence and severity. OBJECTIVE: To investigate the value of choroidal vascular imaging with SD-OCT in diagnosing the presence and predicting the severity of OSA. METHODS: SD-OCT images of 120 patients with OSA were analyzed to extract choroidal biomarkers. Patients were categorized into 4 levels of OSA severity according to their apnea-hypopnea index. ImageJ/FIJI (National Institutes of Health, Bethesda, Maryland, USA) was used to measure choroidal thickness and vascular indices in Haller's and non-Haller's layers across regions on the nasal and temporal sides of the fovea. Thickness ratios of choroidal layers, total choroidal area, choroidal vascularity index, and luminal-to-stromal ratios were compared between individuals without OSA and with different severities of OSA. Analysis of variance, receiver operating characteristic analysis, and logistic regression were employed to evaluate inter-group differences and the predictive value of choroidal parameters. RESULTS: OSA's presence and increasing severity significantly impacted the non-Haller's layer thickness and Haller's/non-Haller's layer thickness ratios, particularly in the nasal region (1000-2500 µm). The nasal 2500 µm region showed the highest discriminative power for severe OSA (AUC = 0.733, P < .001). Logistic regression analysis identified the Haller's/non-Haller's layer thickness ratio at nasal 2500 µm as the most significant predictor of severe OSA (odds ratio = 2.147, P = .002), adjusted for age, gender, and comorbidities. CONCLUSIONS: OSA is associated with choroidal microvascular remodeling, especially nasal to the fovea. This remodeling increases the ratio of Haller's/non-Haller's layer thickness ratio, which may be a potential biomarker for OSA severity. These findings highlight the utility of SD-OCT in non-invasively detecting systemic vascular alterations linked to OSA, supporting its role in early diagnosis and monitoring of disease progression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".