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<p>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.</p>

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Rapid test for detecting red–green color vision deficiencies using a neural network-assisted color-naming task

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月20日

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  • José A. R. Monteiro,
  • Dora N. Marques,
  • João M. M. Linhares,
  • Sérgio M. C. Nascimento

摘要

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.