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.

阿尔茨海默氏症深度学习衍生神经影像内表型遗传学的补充 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遇，例如快速识别 疾病的遗传基础，包括阿尔茨海默病（AD）。例如，家长补助金提出了新的 基于深度学习的方法，用于从神经影像数据中导出 AD 相关的内表型，以及 将这些内表型与遗传数据相关联。它期望发现与 AD 相关的新基因，这可能 有助于更好地了解 AD 的分子基础和潜在的新疗法。 然而，人工智能/机器学习方法可能会在数据收集的设计和实施中带来潜在的偏差， 训练数据以及算法开发。这种偏见可能会导致有问题的发现，并可能进一步 造成健康差距。近年来，学术界和社会界对以下问题的讨论日益频繁： 人工智能道德原则；然而，经验数据或基于证据的机制有限 展示了研究人员对影响研究的伦理问题的知识、态度或观点 人工智能/机器学习算法的开发或他们如何考虑将研究伦理融入到他们的工作中。此外， 如何开展和实施有效的人工智能伦理教育是另一个需要系统科学的问题 询问。 这个拟议的补充将人工智能研究人员和生物伦理学家聚集在一起，创建了第一个测量量表 衡量医学人工智能研究人员对人工智能研究原则（善意、非恶意、 正义和责任）以及他们关于如何使用这些原则来指导道德决策的知识 通过案例研究小插曲，利用人工智能进行阿尔茨海默病研究。 为了针对 AI AD 研究人员创建有效的 AI 伦理教育，我们引入了虚拟现实严肃教育 游戏设计师开发基于 VR 的交互式应用程序，用于道德决策教育 医学人工智能研究。这种互动式、沉浸式的教育材料传递模式已经 与传统教育相比，这会带来更多的参与度、乐趣和更高的效率 渠道。从研究人员和社区咨询委员会收集的信息也将告知 制定该人工智能道德培训计划。 VR应用程序的可用性和有效性将 使用测试后调查和焦点小组进行评估。