Q2VA: A Bayesian Adaptive Method for Visual Acuity Assessment with Tumbling E Stimuli
Published in The 39th IEEE International Symposium on Computer-Based Medical Systems (CBMS-2026), 2026
Recommended citation: Tao, H., Liang, X., Wang, J., Xu, B., Qiu, H., & Xu, P. (Accepted/In press). Q2VA: A Bayesian Adaptive Method for Visual Acuity Assessment with Tumbling E Stimuli. In The 39th IEEE International Symposium on Computer-Based Medical Systems (CBMS-2026) https://scholar.xjtlu.edu.cn/en/publications/q2va-a-bayesian-adaptive-method-for-visual-acuity-assessment-with/
Visual assessment and is critical for the diagnosis and monitoring of ophthalmic pathologies. While traditional tools like the Snellen and ETDRS charts are widely used, they face inherent limitations in balancing testing efficiency with measurement precision. Furthermore, current adaptive digital systems predominantly utilize Latin letter optotypes, creating significant cognitive barriers for specific Chinese demographics, particularly pediatric populations and illiterate adults. To bridge this gap, this study introduces Q2VA, a novel digital visual acuity testing module tailored specifically for the Chinese population, employing the “Tumbling E” optotype to eliminate linguistic bias and integrating a Bayesian adaptive algorithm with an active learning strategy to optimize assessment. The system dynamically estimates both the visual acuity threshold and the selection of stimuli. Validation through Monte Carlo simulations and psychophysical experimental results demonstrates that Q2VA can achieve high accuracy and precision of measurement within just 15 trials. The system maintains a low standard deviation (0.02 LogMAR) and high sensitivity to 0.03 LogMAR changes. Q2VA has the potential to provide a robust, efficient, and population-inclusive solution for high-precision visual acuity monitoring in eye clinics.