AI-based color vision screening and educational counseling for high school students: An experimental study

Clinical Snapshot

Key Findings

• P Value: p > 0.05 for between-group comparison of system evaluation scores between students with and without CVD (non-significant)

• Effect Size: Not reported quantitatively; described qualitatively as 'high concordance'

• Primary Outcome: CVD prevalence of 3.23% (n=17/527) identified by the mobile application; 'high concordance' reported between app-based and conventional Ishihara screening — specific agreement statistics not provided in abstract

• Nnt Or Sensitivity: Sensitivity, specificity, PPV, NPV, and Cohen's kappa not reported — critical omission for a diagnostic agreement study

• Confidence Interval: Not reported for any outcome

Clinical Application

mHealth delivery via Android devices is pragmatically feasible in school settings with high smartphone penetration. However, clinical adoption requires: (1) validated screen calibration protocols, (2) demonstrated sensitivity/specificity against a gold-standard reference (e.g., anomaloscope or Farnsworth-Munsell), (3) optometry or ophthalmology oversight for positive cases, and (4) iOS compatibility for broader reach In Australia, CVD screening is not currently mandated in school health programs, though RACGP guidelines acknowledge its relevance for career guidance (particularly for aviation, defence, maritime, and emergency services careers). The TGA would require formal regulatory approval for any diagnostic medical device app before clinical deployment. The Australian context differs substantially from Vietnam: higher access to optometric services, Medicare-subsidised eye examinations, and established referral pathways mean the primary value proposition of this app (low-resource scalability) is less compelling. However, the concept of integrating CVD screening with career counseling has merit for rural and remote Australian schools with limited access to optometrists. The PBS does not subsidise CVD-specific interventions as there is no pharmacological treatment. RACGP would likely recommend this as a supplementary screening tool only, with mandatory referral to a registered optometrist for confirmatory testing. High school-aged adolescents in low-resource settings where conventional CVD screening infrastructure is limited; potentially applicable to school health programs in developing healthcare systems

Abstract

Color vision deficiency (CVD) can negatively affect students' learning experiences and career choices; however, combined screening and educational counseling remain challenging in school settings, particularly in low-resource contexts. Conventional paper-based color vision tests are time-consuming, difficult to standardize, and rarely linked to individualized educational or career guidance. This study aimed to design, implement, and validate an Android-based mobile application that integrates CVD screening with artificial intelligence (AI)-assisted educational and career counseling for high school students. A cross-sectional study was conducted with 527 high school students in Danang, Viet Nam. The application incorporated a digital Ishihara-based screening module and an AI-assisted counseling component. Screening outcomes were compared with conventional Ishihara testing to evaluate agreement, while students' perceptions of usability, usefulness, and counseling relevance were assessed using a validated questionnaire. Statistical analyses included descriptive statistics, independent-samples t-tests, reliability analysis using Cronbach's alpha, and exploratory factor analysis. Seventeen students (3.23%) were identified as having CVD, including red-green, protan, deutan, and achromatopsia (complete CVD). The mobile application demonstrated high concordance with conventional screening results. No statistically significant differences were observed between students with and without CVD in overall system evaluation scores (p > 0.05). The questionnaire showed good internal consistency (Cronbach's alpha = 0.857) and a clear multi-factor structure reflecting students' acceptance of AI-assisted educational and career counseling. These findings indicate that the proposed mHealth application offers a feasible and scalable approach for school-based CVD screening combined with personalized educational and career guidance. The system may serve as a supportive decision-making tool for students and counselors, while further validation under diverse real-world conditions and across different mobile devices is recommended.

References

1. 1.Vinh, D., & Yen, P. T. (2026). AI-based color vision screening and educational counseling for high school students: An experimental study. PLOS ONE. https://doi.org/10.1371/journal.pone.0353871