Achieving Model Fairness on Automatic Primary Open-angle Glaucoma Screening

项目概要/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 在非洲裔和西班牙裔美国人中。由于POAG导致的视功能丧失是不可逆的， 估计POAG的风险，防止进一步的视力丧失。最近，人们越来越担心， 预测模型可能反映和放大人类偏见，并降低其性能的质量，如果使用在 用于病人分诊的临床管道。受患者疾病表现的已知差异的影响，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表现出系统的 子群体的偏差。消除这种偏见的流行方法表明， 人口统计学中的积极案例有助于模型在验证中表现得更好。然而，收集新数据 往往缺乏人口代表性。响应NOT-EY-22-004（研究寻址 眼睛和视力健康公平性/健康差异）和PAR-22-141（现有数据集的次要分析）， 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风险估计模型，而无需人口统计学平衡数据集。基于我们 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 计划执行特定目标：1）研究POAG风险估计中的“算法偏差”，2）检查 “转移偏差”的影响，从有偏见的人口统计平衡的数据。本报告中提出的研究 项目是新颖和创新的，因为现有数据的二次分析提供了更多的见解， POAG健康差异。Aim 1将是第一个对基于DL的 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POAG预测设置中可能出现的偏差转移，即使POAG数据集 是明确去偏置的。我们认为，我们的模型提供了简单，可解释，易于检查的框架 以便更好地评估受保护人群的POAG风险。该项目的预期成果是一个全面的 该框架旨在通过改善少数群体的推理表现来减轻不公平的影响。成功 该项目的研究将通过以下方式提供对POAG风险估计的健康差异的额外见解：（1）减少临床 受无意识或有意识偏见污染的决策，以及（2）开发反映学习的全新模型 POAG特征而非患者人口统计学特征，以确保不同人群的稳健性。这个项目是 对于数据科学和临床医学来说，这是非常可行的，并且具有潜在的变革性。

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.