快速测量和机器学习对色彩视觉缺陷的分类
Jingyi He1, Peter J Bex1, Jan Skerswetat1
1Department of Psychology, Northeastern University, USA.
medRxiv : the preprint server for health sciences
|July 3, 2023
概括
彩色视觉缺陷 (CVDs) 使用新的计算机工具FInD (Foraging Interactive D-prime) 检测,提供比传统测试更快,更灵敏的方法来评估视觉变化.
科学领域:
- 眼科医生 眼科 眼科
- 神经科学是一个神经科学.
- 遗传学 是一个遗传学.
背景情况:
- 彩色视觉缺陷 (CVDs) 与遗传变异和神经眼科疾病有关.
- 目前的CVD测量工具缺乏用于跟踪变化的灵敏度和效率.
研究的目的:
- 介绍FInD (食交互式D-prime),一个新的,基于计算机的,自我管理的视力评估工具.
- 评估FInD在彩色视觉测试中的应用,以提高灵敏度和可重复性.
主要方法:
- FInD使用基于信号检测理论的自适应范式,从d-prime分析计算刺激强度.
- 参与者在动态发光噪声中发现了色彩斑块或不同的颜色对.
- 在19个颜色正常和18个颜色异常观察者中,FInD Color任务与HRR和FM100色调测试进行了比较.
主要成果:
- 与典型观察者相比,非典型观察者表现出更高的检测和歧视值.
- 值的升高与特定的CVD类型相对应,允许进行分类.
- 无监督机器学习证实了基于FInD性能的功能性CVD亚型.
结论:
- FInD是一种敏感,可重复和可通用的工具,用于评估色彩视觉.
- 这种新的方法可能会增强基础和临床色彩视觉科学和诊断.
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