Democratizing Health Care in the Metaverse: How Video Games can Monitor Eye Conditions Using the Vision Performance Index
Bibliographic record
Abstract
Objective In a world where digital media is deeply engrained into our everyday lives, there lies an opportunity to leverage interactions with technology for health and wellness. The Vision Performance Index (VPI) leverages natural human-technology interaction to evaluate visual function using visual, cognitive and motor psychometric data over five domains: field of view, accuracy, multi-tracking, endurance, and detection. The purpose of this study was to describe a novel method of evaluating holistic visual function through video-game derived VPI score data in patients with specific ocular pathology. Design Prospective comparative analysis Subjects Participants, and/or Controls: Patients with dry eye disease, glaucoma, cataract, diabetic retinopathy (DR), age-related macular degeneration (AMD), and healthy individuals. Methods The Vizzario Inc software development kit was integrated into two video-game applications, Balloon Pop and Picture Perfect, which allowed for generation of VPI scores. Study participants were instructed to play rounds of each video game, from which a VPI score was compiled. Main Outcome Measures The primary outcome was VPI overall score in each comparison group. VPI component and subcomponent scores, and VPI psychophysical inputs, were also compared. Results VPI scores were generated from 93 patients with macular degeneration (n=10), cataract (n=10), diabetic retinopathy (n=15), dry eye (n=15), glaucoma (n=16), and no ocular disease (n=27). VPI overall score was not significantly different across comparison groups. VPI subcomponent ‘reaction accuracy' score was significantly greater in DR patients (106 ± 13.2) versus controls (96.9 ± 11.5), p=0.0220. VPI subcomponent ‘colour detection' score was significantly lower in patients with DR (96.8 ± 2.5; p=0.0217) and glaucoma (98.5 ± 6.3; p=0.0093) compared to controls (101 ± 11; p=0.0198). Psychophysical measures were statistically significantly different from controls: proportion correct (lower in DR, AMD), contrast errors (higher in cataract, DR), and saturation errors (higher in dry eye). Conclusions VPI scores can be generated from interactions of an ocular disease population with videogames. The VPI may offer utility in monitoring select ocular diseases through evaluation of subcomponent and psychophysical input scores, however future, larger scale studies must evaluate the validity of this tool.
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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 | 0.002 | 0.008 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".