Rapid test for detecting red–green color vision deficiencies using a neural network-assisted color-naming task
Abstract
This study presents and evaluates a rapid digital color vision deficiency test using a color-naming task supported by a neural network model. Thirty-three normal trichromats, 12 dichromats, and 11 anomalous trichromats named 182 natural-scene colors using 11 basic terms. A neural network classified individuals for screening, classification, and diagnosis. Sensitivity and specificity reached 97% and 99% for screening analyzing the full and a 20-color subset, respectively. For classification and diagnosis, accuracy was slightly lower. Results show that color naming can efficiently detect deficiencies, suggesting that a fast and accurate screening test using only 20 colors can be completed in under 2 min.
Fields of Study & Subjects
Visual perception and processing mechanisms
Ophthalmology and Visual Impairment Studies
Categorization
perception
and language
Publication details
- Journal / source
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Volume
- 16
- Issue
- 1
- Publication date
- 20 February 2026
- Language
- EN