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)
基本信息
- 批准号:10599738
- 负责人:
- 金额:$ 32.32万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AdoptedAdultAlgorithmsAlzheimer&aposs DiseaseAlzheimer&aposs disease patientAttitudeAwarenessBeneficenceBioethics ConsultantsBiomedical ResearchCase StudyCommunitiesDataData CollectionDecision MakingDevelopmentDiseaseEducationEducational MaterialsEffectivenessEthical IssuesEthicsEvaluationFamilyFamily CaregiverFocus GroupsGenesGeneticGovernmentImmersionIndividualIntuitionJusticeKnowledgeLeadLearningMeasuresMedicalMethodsModelingMolecularNeurologistNonmaleficenceResearchResearch DesignResearch EthicsResearch PersonnelScientific InquirySocial WorkersSurveysTestingTrainingTraining ProgramsWorkalgorithm developmentbasedeep learningdesigneffectiveness evaluationendophenotypeevidence baseexperienceexperimental studyexplicit biashealth disparityimplicit biasneuroimagingparent grantprofession allied to medicineprogramstoolusabilityvirtual realityvirtual reality environment
项目摘要
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的神经成像内表型的遗传学补充
疾病
摘要
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,包括阿尔茨海默病(AD)。例如,父母补助金提出新的
基于深度学习的方法,用于从神经成像数据中导出AD相关的内表型,以及
将这些内在表型与遗传数据联系起来。它有望发现与AD相关的新基因,
从而更好地了解AD的分子基础和潜在的新治疗方法。
然而,AI/ML方法可能会在数据收集的设计和实施中带来潜在的偏见,
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,学术界和社会上对
道德AI的原则;然而,有有限的经验数据或基于证据的机制,
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。
AI/ML算法的开发,或者他们如何考虑将研究伦理融入他们的工作。此外,委员会认为,
如何开发和提供有效的人工智能伦理教育是另一个需要系统科学研究的问题。
查询
这项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的态度(有益,无害,
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知识
通过使用案例研究小插曲,利用人工智能进行阿尔茨海默病研究。
为了创建面向AI AD研究人员的有效的AI伦理教育,我们引入了虚拟现实严肃
游戏设计师开发一个基于VR的交互式应用程序,用于道德决策教育
医学AI研究这种交互式和沉浸式的教育材料提供模式已经
与传统教育相比,
渠道从研究人员和社区咨询委员会收集的信息也将告知
开发这个人工智能道德培训计划。VR应用程序的可用性和有效性将
使用后测调查和焦点小组进行评估。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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MYRIAM FORNAGE其他文献
MYRIAM FORNAGE的其他文献
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{{ truncateString('MYRIAM FORNAGE', 18)}}的其他基金
Multiethnic Validation of VCID biomarkers in South Texas
德克萨斯州南部 VCID 生物标志物的多种族验证
- 批准号:
10369339 - 财政年份:2021
- 资助金额:
$ 32.32万 - 项目类别:
Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease
阿尔茨海默病深度学习神经影像内表型的遗传学
- 批准号:
10653800 - 财政年份:2021
- 资助金额:
$ 32.32万 - 项目类别:
Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease
阿尔茨海默病深度学习神经影像内表型的遗传学
- 批准号:
10675679 - 财政年份:2021
- 资助金额:
$ 32.32万 - 项目类别:
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:
10827718 - 财政年份:2021
- 资助金额:
$ 32.32万 - 项目类别:
Multiethnic Validation of VCID biomarkers in South Texas
德克萨斯州南部 VCID 生物标志物的多种族验证
- 批准号:
10611823 - 财政年份:2021
- 资助金额:
$ 32.32万 - 项目类别:
Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease
阿尔茨海默病深度学习神经影像内表型的遗传学
- 批准号:
10436262 - 财政年份:2021
- 资助金额:
$ 32.32万 - 项目类别:
Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease
阿尔茨海默病深度学习神经影像内表型的遗传学
- 批准号:
10212068 - 财政年份:2021
- 资助金额:
$ 32.32万 - 项目类别:
Microglial, Inflammatory and Omics Markers of Cerebral Small Vessel Disease in the CHARGE Consortium
CHARGE 联盟中脑小血管疾病的小胶质细胞、炎症和组学标记
- 批准号:
9792270 - 财政年份:2016
- 资助金额:
$ 32.32万 - 项目类别:
Microglial, Inflammatory and Omics Markers of Cerebral Small Vessel Disease in the CHARGE Consortium
CHARGE 联盟中脑小血管疾病的小胶质细胞、炎症和组学标记
- 批准号:
9272153 - 财政年份:2016
- 资助金额:
$ 32.32万 - 项目类别:
ADSP Follow-up in Multi-Ethnic Cohorts via Endophenotypes, Omics & Model Systems
通过内表型、组学对多种族队列进行 ADSP 随访
- 批准号:
9078875 - 财政年份:2016
- 资助金额:
$ 32.32万 - 项目类别:
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