Abstract
Acquired color vision deficiencies may provide useful functional information in monitoring of various diseases. This paper presents a revised, more conservative architecture and baseline evaluation of the Interactive Detection of Color Vision Disorders (idCVD) system. While the current implementation of idCVD focuses on generating randomized color-pair decisions along protan, deutan, and tritan confusion axes and on recording both response correctness and time, in this paper a Bayesian adaptive layer is proposed for selecting subsequent stimuli according to expected information gain. This layer is treated as a design target rather than an empirically validated component. The evaluation covers 317 completed studies from 287 participants, producing 39,447 decision events. The observed trial-level accuracy was 97.79% with rare timeout responses (0.19%). Response time captured information not reducible to accuracy alone, proving that adding a Bayesian layer to the diagnostic methos forms a solid research agenda for clinically validated adaptive self-monitoring of color vision.
Paper Type
Poster
DOI
10.62036/ISD.2026.80
More Than Meets The Eye: An Automated Color Vision Deficiency Detection System For Patient Self-Monitoring
Acquired color vision deficiencies may provide useful functional information in monitoring of various diseases. This paper presents a revised, more conservative architecture and baseline evaluation of the Interactive Detection of Color Vision Disorders (idCVD) system. While the current implementation of idCVD focuses on generating randomized color-pair decisions along protan, deutan, and tritan confusion axes and on recording both response correctness and time, in this paper a Bayesian adaptive layer is proposed for selecting subsequent stimuli according to expected information gain. This layer is treated as a design target rather than an empirically validated component. The evaluation covers 317 completed studies from 287 participants, producing 39,447 decision events. The observed trial-level accuracy was 97.79% with rare timeout responses (0.19%). Response time captured information not reducible to accuracy alone, proving that adding a Bayesian layer to the diagnostic methos forms a solid research agenda for clinically validated adaptive self-monitoring of color vision.
Recommended Citation
Laskowski, M. & Kiersztyn, A.(2026). More Than Meets The Eye: An Automated Color Vision Deficiency Detection System For Patient Self-Monitoring. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.80