There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Robert Robison |
| Institution: | Elder Research |
| Department: | Commercial Data Science |
| Country: | |
| Proposed Analysis: | We have been rewarded a Small Business Innovation and Research (SBIR) grant to develop an innovative platform that incorporates intelligent algorithms for detecting and staging mild cognitive impairment (MCI) in Alzheimer's Disease (AD). This grant was awarded under NIH program announcement PAS-18-187: Advancing Research on Alzheimer’s Disease and Disease-Related Dementias, which further highlights the significant need for development of machine learning tools and sensitive, specific and standardized test batteries for diagnostic screening of MCI. We plan on developing a two layer intelligent algorithm that both 1) determines the most appropriate test suite combination for a subject based on demographics and available medical history, and 2) accurately detects AD presence and stage based on that test battery. Therefore, our analysis will involve using ADNI data to develop Machine Learning models for detecting AD presence and stage using different combinations of various tests and biomarkers based on their price and availability. We will then repeat this process with patients grouped into clusters by demographics and medical history. The end goal is widespread earlier detection and treatment of AD. |
| Additional Investigators | |
| Investigator's Name: | Kirsty Ward |
| Proposed Analysis: | We have been rewarded a Small Business Innovation and Research (SBIR) grant to develop an innovative platform that incorporates intelligent algorithms for detecting and staging mild cognitive impairment (MCI) in Alzheimer's Disease (AD). This grant was awarded under NIH program announcement PAS-18-187: Advancing Research on Alzheimer’s Disease and Disease-Related Dementias, which further highlights the significant need for development of machine learning tools and sensitive, specific and standardized test batteries for diagnostic screening of MCI. We plan on developing a two layer intelligent algorithm that both 1) determines the most appropriate test suite combination for a subject based on demographics and available medical history, and 2) accurately detects AD presence and stage based on that test battery. Therefore, our analysis will involve using ADNI data to develop Machine Learning models for detecting AD presence and stage using different combinations of various tests and biomarkers based on their price and availability. We will then repeat this process with patients grouped into clusters by demographics and medical history. The end goal is widespread earlier detection and treatment of AD. |
| Investigator's Name: | Daniel Brannock |
| Proposed Analysis: | We have been rewarded a Small Business Innovation and Research (SBIR) grant to develop an innovative platform that incorporates intelligent algorithms for detecting and staging mild cognitive impairment (MCI) in Alzheimer's Disease (AD). This grant was awarded under NIH program announcement PAS-18-187: Advancing Research on Alzheimer’s Disease and Disease-Related Dementias, which further highlights the significant need for development of machine learning tools and sensitive, specific and standardized test batteries for diagnostic screening of MCI. We plan on developing a two layer intelligent algorithm that both 1) determines the most appropriate test suite combination for a subject based on demographics and available medical history, and 2) accurately detects AD presence and stage based on that test battery. Therefore, our analysis will involve using ADNI data to develop Machine Learning models for detecting AD presence and stage using different combinations of various tests and biomarkers based on their price and availability. We will then repeat this process with patients grouped into clusters by demographics and medical history. The end goal is widespread earlier detection and treatment of AD. |
| Investigator's Name: | Luke Farley |
| Proposed Analysis: | We have been rewarded a Small Business Innovation and Research (SBIR) grant to develop an innovative platform that incorporates intelligent algorithms for detecting and staging mild cognitive impairment (MCI) in Alzheimer's Disease (AD). This grant was awarded under NIH program announcement PAS-18-187: Advancing Research on Alzheimer’s Disease and Disease-Related Dementias, which further highlights the significant need for development of machine learning tools and sensitive, specific and standardized test batteries for diagnostic screening of MCI. We plan on developing a two layer intelligent algorithm that both 1) determines the most appropriate test suite combination for a subject based on demographics and available medical history, and 2) accurately detects AD presence and stage based on that test battery. Therefore, our analysis will involve using ADNI data to develop Machine Learning models for detecting AD presence and stage using different combinations of various tests and biomarkers based on their price and availability. We will then repeat this process with patients grouped into clusters by demographics and medical history. The end goal is widespread earlier detection and treatment of AD. |
