The Use of Real‐Time Image Stabilization and Augmented Reality Eyewear in the Treatment of Oscillopsia
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The symptom of oscillopsia in patients with bilateral vestibular loss (BVL) can be reduced as dynamic visual acuity (DVA), the reduction in visual acuity during head movement, is improved by using real-time image stabilization, delivered by augmented reality eyewear. SETTING: Tertiary multidisciplinary neurotology clinic. STUDY DESIGN: Prospective experimental study. METHODS: Immersive virtual reality glasses used in combination with a compact digital video camera were used. A software algorithm was developed that used a center-weighted Lucas-Kanade optical flow method to stabilize video in real time. Six patients with BVL were tested for changes in DVA using the eyewear. The ability to read a Snellen chart during a 2-Hz oscillating head rotation DVA test was measured. RESULTS: For combined scores of vertical and horizontal head rotations, the mean number of lines readable at rest was 7.86, which dropped to 2.77 with head movement (a combination of vertical and horizontal perturbations). This increased to a mean of 6.14 lines with the image stabilization software being activated. This difference was statistically significant (P < .001). CONCLUSION: This is the first successful attempt to improve dynamic visual acuity in patients with bilateral vestibular loss. Recent hardware upgrades are promising in improving these results even further.
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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 | 0.000 | 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".