Spatial and Temporal Changes in Choroid Morphology Associated With Long-Duration Spaceflight
Bibliographic record
Abstract
Purpose: Amid efforts to understand spaceflight associated neuro-ocular syndrome (SANS), uncovering the role of the choroid in its etiology is challenged by the accuracy of image segmentation. The present study extended deep learning-based choroid quantification from optical coherence tomography (OCT) to the characterization of pulsatile and topological changes in the macular plane and investigated changes in response to prolonged microgravity exposure. Methods: We analyzed OCT macular videos and volumes acquired from astronauts before, during, and after long-duration spaceflight. Deep learning models were fine-tuned for choroid segmentation and combined with further image processing toward vascularity quantification. Statistical analysis was performed to determine changes in time-dependent and spatially averaged variables from preflight baseline. Results: For 12 astronauts with a mean age of 47 ± 9 years, there were significant increases in choroid thickness and luminal area (LA) averaged over OCT macular video segments. There was also a significant increase in pulsatile LA. For a subgroup of six astronauts for whom inflight imaging was available, choroid volume, luminal volume, and the choroidal vascularity index over the macular region all increased significantly during spaceflight. Conclusions: The findings suggest that localized choroid pulsatile changes occur following prolonged microgravity exposure. They show that the choroid vessels expand in a manner similar to the choroid layer across the macular region during spaceflight, with a relative increase in the space they occupy. The methods developed provide new tools and avenues for studying and establishing effective countermeasures to risks associated with long-duration spaceflight.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".