Achieving Model Fairness on Automatic Primary Open-angle Glaucoma Screening

实现自动原发性开角型青光眼筛查的模型公平性

• 批准号: 10726928
• 负责人: Yifan Peng
• 金额: $ 46.61万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10726928
• 关键词: Address; Adopted; African American population; Area; Artificial Intelligence; Blindness; Clinical; Clinical Medicine; Conscious; Data; Data Science; Data Scientist; Data Set; Dedications; Disease; Ensure; Ethnic Origin; Exhibits; Eye; Health Disparities Research; Hispanic Americans; Human; Learning; Minority; Mission; Modeling; Ophthalmologist; Outcome; Participant; Patient Triage; Patients; Performance; Population Heterogeneity; Primary Open Angle Glaucoma; Public Health; Quality of life; Race; Research; Risk Estimate; Strategic Planning; Systematic Bias; Unconscious State; United States; Validation; Vision; Vision research; algorithm training; algorithmic bias; demographics; ethnic minority; experience; health disparity; health equity; health information technology; improved; innovation; insight; loss of function; novel; outcome disparities; predictive modeling; prevent; racial minority; response; screening; secondary analysis; sex; success

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.

项目摘要/摘要 在美国，原发性开角青光眼（POAG）是失明的主要原因，尤其是 在非洲和西班牙裔美国人中。因为POAG的视觉功能丢失是不可逆的，所以这对于 估计POAG的风险并防止进一步的视力丧失。最近，人们越来越担心 如果在 患者分类的临床管道。受患者的疾病表现差异的激励，例如 性别和种族/种族，这项研究假设在现有数据集中培训的算法将存在系统 亚群中的偏见。消除此类偏见的流行方法表明有更多数量 跨人口统计的积极病例有助于模型在验证方面的表现更好。但是，收集新数据 通常会遭受缺乏人口统计的表现。为了回应NOT-EY-22-004（研究解决方案 眼睛和视力健康平等/健康差异）和PAR-22-141（现有数据集的次要分析）， 该项目将开发并验证一种新的人工智能方法来提高预测性的公平性 POAG风险估计的模型，无需人口统计数据集。基于我们 初步数据以及我们与数据科学家和眼科医生的跨学科团队的经验，我们 计划执行具体目的：1）在POAG风险估计中研究“算法偏见”和2）检查 从偏见到人口平衡数据的“转移偏见”的影响。提出的研究 项目是新颖和创新的，因为现有数据的二级分析为您提供了更多的见解 POAG健康差异。 AIM 1将是第一个对基于DL的算法偏差进行系统研究的研究 POAG预测模型并确定导致建模公平的因素。 AIM 2将是第一个研究 检查POAG预测设置可能会出现偏差转移，即使在POAG数据集时也可能发生 明确偏离偏见。我们认为我们的模型提供了简单，可解释且易于检查的框架 为了更好地估计受保护群体的POAG风险估计。该项目的预期结果是整体 通过改善少数民族的推论绩效来减轻不平等影响的框架。成功 该项目将通过（1）减少临床对POAG风险估计的健康分布提供更多的了解 决定被无意识或有意识的偏见污染，以及（2）发展反映学识渊博的全新模型 POAG特征但没有患者人群，以确保潜水员人群的稳健性。这个项目是 对于数据科学和临床医学而言，高度可行性且有潜在的变化。