Reference |
Purpose |
Method |
Outcome |
Gap |
[9] |
Assess gamified approach for visual acuity in children |
Mobile game- based assessment |
Improved engagement and potential accuracy in pediatric visionassessment |
Limited specificity to particular disorders |
[19] |
Measure Contrast Sensitivity Function (CSF)in amblyopic children |
Game-based CSF test |
Differentiation between healthy and amblyopiceyes |
Potential limitations of game format |
[20] |
Evaluate self- administered contrast sensitivity tests inchildren |
Gamified iPad app |
Enhanced accessibility to vision screening |
Variability in device and environmentusage |
[23] |
Identify dyslexia througheye-tracking analysis |
Eye-trackingtechnology |
Objective dyslexiascreening method |
Validation acrossdiverse populations |
[12] |
Utilize eye-tracking and machine learning fordyslexia screening |
Machine learning models on eye-tracking data |
High accuracy in dyslexia detection |
Standardization across differentenvironments |
[24] |
Investigate Beynex app for cognitive decline screening |
Gamified cognitive tests |
Moderate correlation with MoCA ratings |
User demographic diversity and clinicalvalidation |
[3] |
Explore XR for visionfunction testing |
XR |
Enhanced engagementand accessibility |
Standardization andclinical integration |
[22] |
Develop gamified onlinetest with machine-learning for dyslexia screening |
Online gamifiedtest with ML model |
Achieved over 80%accuracy in dyslexia detection |
Validation acrossdifferent datasets and environments |
Table 1: Summary of Literature Review
Tables at a glance