快速测量和机器学习对色视障碍的分类
Jingyi He1, Peter J Bex1, Jan Skerswetat1
1Department of Psychology, Northeastern University, Boston, Massachusetts, USA.
概括
一种新的计算机视觉测试,Foraging Interactive D-prime (FInD),可以准确地检测和分类遗传色彩视觉缺陷 (CVD). 这种快速,自我管理的工具为色彩视觉科学和临床应用提供了有前途的进步.
科学领域:
- 眼科医生 眼科 眼科
- 遗传学 遗传学 是一个
- 计算机科学 计算机科学
背景情况:
- 色彩视觉缺陷 (CVD) 与遗传变异和获得的神经眼科疾病有关.
- 目前的CVD测试检测到存在,但缺乏类型/严重性的量化.
研究的目的:
- 介绍食交互式D-prime (FInD),一种新的基于计算机的视力评估工具,用于彩色视力测试.
- 评估FInD的灵敏度,可重复性和分类心血管疾病类型和严重程度的能力.
主要方法:
- FInD使用基于信号检测理论的自适应范式,在动态发光噪声中使用色斑.
- 参与者检测到斑块或区分不同的颜色;刺激强度是从d-prime分析中得出的.
- 与Hardy-Rand-Rittler和Farnsworth-Munsell100色调测试中的FInD比较. 在颜色正常和非典型观察者中.
主要成果:
- 非典型的观察者比典型的观察者有更高的检测和歧视值.
- 值的升高与特定的CVD类型相对应,使其能够进行分类.
- 无监督机器学习证实了基于FInD结果的功能性CVD亚型.
结论:
- FInD可靠地检测遗传性心血管疾病,并量化其类型和严重程度.
- 这种新的工具显示了基础和临床彩色视觉研究的潜力.
- FInD提供了一种可通用,快速和自主管理的视力评估方法.
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