ArticleScientific reports2026
Rapid test for detecting red-green color vision deficiencies using a neural network-assisted color-naming task.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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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.
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