海外国家和地区规范数据库的公平性和通用性:比较分析.
Luis Filipe Nakayama1,2, Lucas Zago Ribeiro3, Juliana Angelica Estevão de Oliveira3
1Laboratory of Computational Physiology, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, United States of America. luisnaka@mit.edu.
International journal of retina and vitreous
|August 22, 2023
概括
光学连贯断层扫描 (OCT) 机器中的规范数据库显示多样性有限和静态框架. 这就需要谨慎地解释OCT的正常性,并要求更具代表性的数据集用于医疗保健中的机器学习.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 光学连贯性断层扫描 (OCT) 依赖于规范性数据库进行正常性评估.
- 这些数据库通过将患者测量结果与参考人群进行比较,提供了彩色编码的尺度.
- 医疗保健中的监督机器学习算法严重依赖于准确和具有代表性的数据标签和报告.
研究的目的:
- 评估和比较来自不同OCT机器 (Cirrus,Avanti,Spectralis,Triton) 的规范数据库.
- 分析这些数据库中使用的患者人口统计,纳入/排除标准,多样性和统计方法.
- 评估现有的OCT规范数据库的公平性和通用性.
主要方法:
- 数据是从Cirrus,Avanti,Spectralis和Triton OCT设备的FDA批准和设备手册中提取的.
- 与之比较的关键变量包括数据库大小,参与者人口统计 (性别,种族,种族,年龄),纳入/排除标准,参与国和多样性指数.
- 还分析了数据库构建中使用的统计方法.
主要成果:
- 阿凡蒂拥有最大的规范数据库 (640个眼睛).
- 包括/排除标准在各个数据库中一致,一般包括成年人,不包括病理病例.
- 在Spectralis中,白人参与者比例最高 (79.7%),亚洲的Cirrus (24%) 和黑色的Triton (22%) 占比最高. 阿凡蒂拥有最高的整体种族多样性.
- 所有数据库都使用回归模型进行统计分析,性别多样性在数据集中是可比的.
结论:
- 现有的OCT规范数据库是静态的,更新能力有限,对新模块缺乏支持.
- 鉴于当前数据库的静态性和人口统计局限性,对OCT正常性的解释需要谨慎.
- 开发更多样化,更具代表性和更开放的数据集至关重要,特别是为了推进眼科领域的监督机器学习.
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