Integrating Virtual Reality Visual Perimetry Into Clinical Practice: A Review of Devices, Applications, and Limitations
Bibliographic record
Abstract
Visual field testing has long been a cornerstone of glaucoma diagnosis, monitoring, and management. The evolution of perimetry, from the early Tangent screen formalized by Julius Hirschberg in the 1870s to modern standard automated perimetry (SAP) such as the Humphrey Visual Field Analyzer (HFA), has aimed to improve accuracy and accessibility. In the 1940s and 1950s, the Goldmann perimeter and Tübingen perimeter were developed, with the Goldmann retaining a limited but important role in specific clinical scenarios. The Tübingen perimeter is now rarely used. By the 1980s, automated perimetry had become the standard, leveraging computational advances to reduce human involvement while preserving the spatial testing strategies introduced by earlier kinetic methods. Devices such as the Humphrey and Octopus perimeters became widely adopted and remain in clinical use today. Among these, the HFA is widely regarded as the gold standard for automated visual field testing. Although the HFA is the gold standard for automated perimetry, it has well-known limitations. The device is expensive, requires substantial physical space, and requires a trained technician to operate. Importantly, many patients find the test uncomfortable or frustrating and often dread the experience. It is rare to encounter a patient who enjoys visual field testing, and poor tolerance can lead to unreliable results. Nevertheless, perimetry remains a cornerstone of glaucoma care, offering functional insights not captured by structural imaging alone. Improving patient compliance and enhancing the test experience are therefore critical.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".