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

> **NIH NIH U01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2022 · $323,239

## Abstract

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.

## Key facts

- **NIH application ID:** 10599738
- **Project number:** 3U01AG070112-02S2
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** MYRIAM FORNAGE
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $323,239
- **Award type:** 3
- **Project period:** 2021-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10599738

## Citation

> US National Institutes of Health, RePORTER application 10599738, Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease (Parent grant) (3U01AG070112-02S2). Retrieved via AI Analytics 2026-06-15 from https://api.ai-analytics.org/grant/nih/10599738. Licensed CC0.

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