机器学习从眼科成像中获得的视网膜色素评分表明种族不是生物学
Anand E Rajesh1,2, Abraham Olvera-Barrios3, Alasdair N Warwick3,4
1Department of Ophthalmology, University of Washington, Seattle, WA, USA.
Nature communications
|January 2, 2025
概括
研究人员开发了一种新的视网膜色素评分 (RPS),用于测量成像中的眼睛色素多样性. 这一指标为AI模型开发和验证提供了种族的生物替代方案.
科学领域:
- 眼科成像 眼科成像
- 生物医学数据科学是生物医学数据科学.
- 遗传学 是一个遗传学.
背景情况:
- 眼科成像表型多样性存在有限的指标.
- 种族经常被用作生物变异性的代理,这可能是不准确的.
- 需要对视网膜色素的客观量化.
研究的目的:
- 为了导出和验证一个连续的,用于视网膜色素的测量度量.
- 评估这个指标在各种数据集和AI算法开发中的实用性.
- 将人口统计学变量与临床成像特征分开.
主要方法:
- 视网膜色素评分 (RPS) 的发展从彩色底部照片.
- 使用大型流行病学研究 (英国生物银行,EPIC-Norfolk) 与人口和遗传数据验证RPS.
- 在坦桑尼亚,澳大利亚和中国数据集中复制RPS.
- 对RPS的全基因组关联研究 (GWAS).
主要成果:
- RPS从底部图像中量化了视网膜色素化程度.
- GWAS确定了20个与色素相关的位置,其中8个复制.
- 观察到RPS和种族之间的强烈关联,但有显著的重叠.
- RPS提供了一种与传统人口统计学变量不同的生物测量方法.
结论:
- RPS是视网膜色素化验证的连续指标.
- RPS可以作为一个有价值的工具来量化AI开发中的数据集多样性.
- 确保AI模型的适当包含和可解释性对于评估部署模型至关重要.
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