Analysis of Pulsatile Retinal Movements by Spectral-Domain Low-Coherence Interferometry: Influence of Age and Glaucoma on the Pulse Wave
Bibliographic record
Abstract
Recent studies have shown that ocular hemodynamics and eye tissue biomechanical properties play an important role in the pathophysiology of glaucoma. Nevertheless, better, non-invasive methods to assess these characteristics in vivo are essential for a thorough understanding of degenerative mechanisms. Here, we propose to measure ocular tissue movements induced by cardiac pulsations and study the ocular pulse waveform as an indicator of tissue compliance. Using a novel, low-cost and non-invasive device based on spectral-domain low coherence interferometry (SD-LCI), we demonstrate the potential of this technique to differentiate ocular hemodynamic and biomechanical properties. We measured the axial movement of the retina driven by the pulsatile ocular blood flow in 11 young healthy individuals, 12 older healthy individuals and 15 older treated glaucoma patients using our custom-made SD-OCT apparatus. The cardiac pulse was simultaneously measured through the use of an oximeter to allow comparison. Spectral components up to the second harmonic were obtained and analyzed. For the different cohorts, we computed a few parameters that characterize the three groups of individuals by analyzing the movement of the retinal tissue at two locations, using this simple, low-cost interferometric device. Our pilot study indicates that spectral analysis of the fundus pulsation has potential for the study of ocular biomechanical and vascular properties, as well as for the study of ocular disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".