Achieving Model Fairness on Automatic Primary Open-angle Glaucoma Screening

项目摘要/摘要 在美国，原发性开角型青光眼(POAG)是导致失明的主要原因，尤其是 在非洲和西班牙裔美国人中。由于POAG导致的视觉功能丧失是不可逆转的，因此 评估POAG的风险，防止进一步的视力丧失。最近，人们越来越担心 如果使用预测模型，可能会反映和放大人类的偏见，并降低他们的表现质量 病人分类的临床流水线。受已知疾病表现差异的激励，例如 性别和种族/民族，这项研究假设在现有数据集上训练的算法将显示系统性 亚群中的偏向。消除这种偏见的流行方法表明，拥有更多的 人口统计数据中的阳性案例有助于模型在验证中表现得更好。然而，收集新数据 经常受到缺乏人口代表性的困扰。响应NOT-EY-22-004(研究寻址 眼睛和视力健康公平/健康差异)和PAR-22-141(现有数据集的二次分析)， 该项目将开发和验证一种新的人工智能方法，以提高预测的公平性 POAG风险评估模型，不需要人口统计平衡的数据集。基于我们的 初步数据和我们与数据科学家和眼科医生组成的跨学科团队的经验，我们 计划执行具体目标：1)研究POAG风险估计中的“算法偏差”；2)检查 “转移偏差”对人口统计平衡数据的影响。在这篇文章中提出的研究 项目具有新颖性和创新性，因为对现有数据的二次分析提供了对 POAG健康差距。Aim 1将第一个对基于数字图书馆的算法偏差进行系统研究 POAG预测模型，并确定影响模型公平性的因素。目标2将是第一个研究 检查偏置转移可能出现在POAG预测设置中，并且即使在POAG数据集的情况下也可能发生 是明确的无偏见的。我们认为，我们的模型提供了简单、可解释和易于检查的框架 以便更好地评估受保护群体的POAG风险。这个项目的预期结果是一个整体的 通过改善对少数群体的推理表现来减轻不平等的影响的框架。成功之路 将通过以下方式提供对POAG风险评估的健康差异的进一步洞察：(1)减少临床 被无意识或有意识的偏见玷污的决定，以及(2)开发反映所学知识的全新模式 POAG具有但不是患者的特征，以确保跨不同人群的稳健性。这个项目是 对数据科学和临床医学都具有高度可行性和潜在的变革性。

Project summary/abstract In the United States, primary open-angle glaucoma (POAG) is the leading cause of blindness, especially among African and Hispanic Americans. Because visual function loss from POAG is irreversible, it is critical to estimate the risk of POAG and prevent further vision loss. Recently, there has been growing concern that the predictive model may reflect and amplify human bias and reduce the quality of their performance if used in the clinical pipeline for patient triage. Motivated by known differences in disease manifestation in patients such as sex and race/ethnicity, this study hypothesizes that algorithms trained on existing datasets will exhibit systematic biases in subpopulations. Popular approaches to remove such biases suggested that having a greater number of positive cases across demographics helped models perform better in validation. However, collecting new data often suffers from a lack of demographic representation. In response to NOT-EY-22-004 (Research Addressing Eye and Vision Health Equity/Health Disparities) and PAR-22-141 (Secondary Analysis of Existing Datasets), this project will develop and validate a new artificial intelligence approach to improve the fairness of the predictive model on POAG risk estimation without the need for demographically balanced datasets. Based on our preliminary data and our experience with an interdisciplinary team of data scientists and ophthalmologists, we plan to execute specific aims: 1) studying “algorithmic bias” in the POAG risk estimation and 2) examining the impact of “transfer bias” from the biased to the demographically balanced data. The studies proposed in this project are novel and innovative because the secondary analyses of existing data provide additional insight into POAG health disparities. Aim 1 will be the first to perform a systematic study of algorithm bias in the DL-based POAG predictive models and identify the factors contributing to model fairness. Aim 2 will be the first study to examine that bias transfer may arise in the POAG prediction setting and can occur even when the POAG dataset is explicitly de-biased. We argue that our models provide simple, interpretable, and easily checkable frameworks to allow better POAG risk estimation for protected groups. The expected outcome of this project is a holistic framework to mitigate the impacts of inequity by improving the inference performance for minorities. The success of this project will provide additional insight into health disparities of POAG risk estimation by (1) reducing clinical decisions tainted by unconscious or conscious bias, and (2) developing brand-new models that reflect learned POAG features but not patient demographic to ensure robustness across diverse populations. This project is highly feasible and potentially transformative for both data science and clinical medicine.