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: | Arun Thirunavukarasu |
| Institution: | Singapore Eye Research Institute |
| Department: | Artificial Intelligence and Digital Innovation |
| Country: | |
| Proposed Analysis: | We aim to use machine learning methods to determine whether a) MRI can be utilised as a more accurate diagnostic tool in Alzheimer's disease (advanced and/or mild); b) automated machine learning methods are feasible for constructing effective diagnostic models, with comparisons between code-free, code-minimal, and code-intensive platforms; c) mild cognitive impairment, early/late or general, is diagnosable with MRI features alone, again utilising automated machine learning techniques. The machine learning techniques planned are deep learning, specifically utilising convoluted neural networks to analyse the MRI data. |
| Additional Investigators | |
| Investigator's Name: | Kabilan Elangovan |
| Proposed Analysis: | Engaging with similar analysis to Arun Thirunavukarasu, focussing on code-intensive automated machine learning, and conventional machine learning techniques. Deep learning convoluted neural networks are planned. |
| Investigator's Name: | Laura Gutierrez |
| Proposed Analysis: | Engaging with similar analysis to Arun Thirunavukarasu, focussing on code-minimal automated machine learning. Deep learning convoluted neural networks are planned. |
| Investigator's Name: | Daniel Ting |
| Proposed Analysis: | Engaging with similar analysis to Arun Thirunavukarasu, across code-free, code-minimal, and code-intensive platforms. Deep learning convoluted neural networks are planned to assess the diagnostic potential of MRI in early and late-stage Alzheimer's disease. |
