SCH: Quantifying and mitigating demographic biases of machine learning in real world radiology

SCH：量化和减轻现实世界放射学中机器学习的人口统计偏差

• 批准号: 10818941
• 负责人: JEREMIAS SULAM
• 金额: $ 31.85万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10818941
• 关键词: Address; Age; Algorithms; Anatomy; Area; Artificial Intelligence; Binding; Breast Cancer Detection; Certification; Chest imaging; Clinical/Radiologic; Complement; Complex; Data; Data Set; Development; Diagnosis; Diagnostic Imaging; Disease; Disparity; Engineering; Ensure; Ethics; Evaluation; Explosion; Future; Generations; Health; Health Policy; Image; Individual; Instruction; Knowledge; Location; Machine Learning; Malignant neoplasm of lung; Measures; Medical Imaging; Medical center; Medicine; Methodology; Methods; Modernization; Monitor; Outcome; Performance; Population; Procedures; Proxy; Public Health Applications Research; Race; Radiology Specialty; Recommendation; Research; Sampling; Science; Screening for cancer; Screening procedure; Series; System; Technology; Thoracic Radiography; Time; Training; Underrepresented Populations; Validation; access disparities; algorithmic bias; artificial neural network; biological sex; breast imaging; cancer diagnosis; cancer imaging; clinically relevant; deep neural network; demographics; diagnostic accuracy; diagnostic tool; disease classification; disparity reduction; health disparity; high risk; imaging modality; improved; lung cancer screening; machine learning algorithm; machine learning method; machine learning model; malignant breast neoplasm; novel; population based; prediction algorithm; predictive modeling; prevent; public health relevance; real world application; screening; screening program; tool

PROJECT SUMMARY (See instructions): The application of modern machine learning algorithms in radiology continues to grow, as these tools represent potential huge improvements in efficiency, accessibility and accuracy of diagnostic and screening tools. At the same time, these increasingly complex machine learning models can have biased predictions against individuals of under-represented demographic groups, potentially perpetuating pre-existing health disparities. Such fairness concerns are particularly important in public health applications that focus on large scale population-based screening, as in cancer screening for breast and lung cancer. In these settings, it is paramount to understand how often machine learning screening algorithms can be unfair and biased, and how to mitigate these disparities. This proposal will develop tools to quantify, correct, and analyze the biases of predictive algorithms in relation to different demographic groups in real world settings. In particular, we will develop analysis and algorithms to quantify the violation of fairness by a machine learning model in situations where information about the sensitive attribute itself (such as biological sex, race or age) are not directly observable, and we will provide algorithms that correct for their worst-case fairness violations. We will analyze our tools under distribution shifts, whereby differences in populations exist, as is common in large scale cancer screening programs. This project will also perform inference on the training samples and features most highly associated with fairness violations, thereby providing guidance on the development of solutions to prevent biased algorithms in the future. Our tools will be validated on a variety of large real-world radiology datasets spanning multiple imaging modalities, including general chest X-ray datasets that include lung cancer diagnoses (CheXpert and MIMIC-CXR), as well as the Emory Breast Cancer Imaging Dataset (EMBED) and the National Lung Cancer Screening Trial, evaluating and correcting disparities for predictive algorithms with respect to biological sex (where appropriate), race, and age. The results of this project will establish critical knowledge about the propensity of machine learning models for medical imaging diagnosis and cancer screening to be unfair and biased, as well as foundational tools to quantify and mitigate these biases in these potentially game-changing technologies.

项目总结（见说明）： 现代机器学习算法在放射学中的应用持续增长，因为这些工具 代表着诊断的效率、可及性和准确性的潜在巨大改进， 筛选工具与此同时，这些日益复杂的机器学习模型可能会有偏差， 针对代表性不足的人口群体的个人的预测，可能会使 之前存在的健康差距。这种公平性问题在公共卫生领域尤其重要 专注于大规模基于人群的筛查的应用，例如乳腺癌筛查和 肺癌在这些环境中，了解机器学习筛选的频率至关重要 算法可能是不公平和有偏见的，以及如何减轻这些差异。该提案将发展 工具来量化，纠正和分析预测算法的偏见， 在真实的世界环境中的人口群体。特别是，我们将开发分析和算法， 量化机器学习模型在以下情况下对公平性的违反： 敏感属性本身（如生物性别，种族或年龄）是不能直接观察到的，我们将 提供算法来纠正最坏情况下的公平性违规。我们将分析我们的工具， 分布变化，从而存在人群差异，这在大规模癌症筛查中很常见 程序.该项目还将对训练样本和特征进行最高程度的推理 与违反公平有关，从而为制定解决方案提供指导， 有偏见的算法我们的工具将在各种大型现实世界的放射学上得到验证 跨多种成像模式的数据集，包括包含肺部的一般胸部X射线数据集 癌症诊断（CheXpert和MIMIC-CXR），以及Emory乳腺癌成像数据集 （EMBED）和国家肺癌筛查试验，评估和纠正 关于生物性别（在适当的情况下）、种族和年龄的预测算法。的结果 该项目将建立关于机器学习模型用于医疗的倾向的关键知识 影像诊断和癌症筛查是不公平和有偏见的，以及量化的基本工具， 并减轻这些可能改变游戏规则的技术中的偏见。