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Record W4379470495 · doi:10.1016/j.xops.2023.100349

Democratizing Health Care in the Metaverse: How Video Games can Monitor Eye Conditions Using the Vision Performance Index

2023· article· en· W4379470495 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOphthalmology Science · 2023
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsPrism Eye InstituteUniversity of Toronto
FundersNational Eye InstituteMissouri University of Science and TechnologyResearch to Prevent Blindness
KeywordsMacular degenerationMedicineVideo gameGlaucomaLeverage (statistics)Index (typography)OphthalmologyOptometryArtificial intelligenceComputer scienceMultimedia

Abstract

fetched live from OpenAlex

ObjectiveIn 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.DesignProspective comparative analysisSubjectsParticipants, and/or Controls: Patients with dry eye disease, glaucoma, cataract, diabetic retinopathy (DR), age-related macular degeneration (AMD), and healthy individuals.MethodsThe 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 MeasuresThe primary outcome was VPI overall score in each comparison group. VPI component and subcomponent scores, and VPI psychophysical inputs, were also compared.ResultsVPI 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).ConclusionsVPI 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.372
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it