Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease (Parent grant)

阿尔茨海默氏病深度学习衍生的神经影像内表型的遗传学（家长资助）

• 批准号: 10599738
• 负责人: MYRIAM FORNAGE
• 金额: $ 32.32万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10599738
• 关键词: Adopted; Adult; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; Attitude; Awareness; Beneficence; Bioethics Consultants; Biomedical Research; Case Study; Communities; Data; Data Collection; Decision Making; Development; Disease; Education; Educational Materials; Effectiveness; Ethical Issues; Ethics; Evaluation; Family; Family Caregiver; Focus Groups; Genes; Genetic; Government; Immersion; Individual; Intuition; Justice; Knowledge; Lead; Learning; Measures; Medical; Methods; Modeling; Molecular; Neurologist; Nonmaleficence; Research; Research Design; Research Ethics; Research Personnel; Scientific Inquiry; Social Workers; Surveys; Testing; Training; Training Programs; Work; algorithm development; base; deep learning; design; effectiveness evaluation; endophenotype; evidence base; experience; experimental study; explicit bias; health disparity; implicit bias; neuroimaging; parent grant; profession allied to medicine; programs; tool; usability; virtual reality; virtual reality environment

Supplement to Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer’s Disease. Abstract AI/ML provides unprecedented opportunities for biomedical researchers, such as the quick identification of the genetic basis of diseases, including Alzheimer’s Disease (AD). For instance, the parent grant proposes new deep learning based approaches for deriving AD-relevant endophenotypes from neuroimaging data, and associating these endophenotypes to genetic data. It expects to discover new genes relevant to AD which may lead to a better understanding of the molecular basis of AD and potential new treatments. However, AI/ML methods could bring potential biases in the design and implementation of data collection, training data, as well as algorithm development. Such biases may lead to problematic findings and may further contribute to health disparity. Recent years have witnessed the heightened scholarly and societal discussion of principles of ethical AI; however, there is limited empirical data or evidence-based mechanisms that have demonstrated researchers’ knowledge, attitudes, or perspectives on ethical issues that impact the development of AI/ML algorithms or how they consider integrating research ethics into their work. Furthermore, how to develop and deliver effective AI ethics education is another issue that requires systematic scientific inquiry. This proposed supplement brings together AI researchers and bioethicists to create the first measure scale to measure medical AI researchers’ attitudes toward AI research principles (beneficence, non-maleficence, justice, and responsibility) and their knowledge about how to use these principles to guide ethical decision making in conducting Alzheimer’s Disease Research using AI through the use of case study vignettes. To create effective AI ethics education geared toward AI AD researchers, we bring in virtual-reality serious game designers to develop a VR-based, interactive application for education on ethical decision-making medical AI in research. Such an interactive and immersive mode of delivering educational materials has been shown to lead to more engagement, enjoyment, and higher effectiveness, compared to traditional educational channels. Information collected from researchers as well as a community advisory board will also inform the development of this AI ethics training program. The usability and effectiveness of the VR application will be evaluated using post-test survey and focus group.

对阿尔茨海默氏症的深度学习衍生神经影像型的遗传学补充 疾病。 抽象的 AI/ML为生物医学研究人员提供了前所未有的机会，例如快速识别 疾病的遗传基础，包括阿尔茨海默氏病（AD）。例如，父母赠款提案新 基于深度学习的方法，用于从神经影像数据中得出与广告相关的内型型， 将这些内表型与遗传数据相关联。它希望发现与AD相关的新基因 可以更好地理解AD的分子基础和潜在的新疗法。 但是，AI/ML方法可以在数据收集的设计和实施中带来潜在的偏见， 培训数据以及算法开发。这种偏见可能会导致有问题的发现，并可能进一步 对健康差异的贡献。近年来见证了对科学和社会讨论的高度讨论 道德AI的原则；但是，具有有限的经验数据或基于证据的机制 展示了研究人员的知识，参与者或观点，以影响影响的道德问题 AI/ML算法的开发或他们如何考虑将研究整合到工作中。此外， 如何发展和提供有效的AI伦理教育是另一个需要系统科学的问题 询问。 该提出的补充剂汇集了AI研究人员和生物伦理学家，以创建第一个测量量表 衡量医学AI研究人员的参与者对AI研究原则（福利，非遗憾， 正义和责任）以及他们有关如何使用这些原则指导道德决定的知识 通过使用案例研究viNettes，使用AI进行AI进行阿尔茨海默氏病研究。 为了创建针对AI AD研究人员的有效的AI道德教育，我们使虚拟现实认真 游戏设计师将开发基于VR的，互动式的教育应用程序，以进行道德决策 研究中的医学AI。这种交付教育材料的互动和沉浸式模式已经 与传统的教育相比 频道。从研究人员那里收集的信息以及社区顾问委员会还将通知 制定该AI道德培训计划。 VR应用程序的可用性和有效性将是 使用测试后调查和焦点小组进行评估。