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: | Mohammed Alsubaie |
| Institution: | University of Newcastle |
| Department: | Computer |
| Country: | |
| Proposed Analysis: | This project will achieve breakthroughs in early diagnosis of dementia based on both the latest machine learning techniques and the expertise in neuropsychology and epidemiology. Current practice in diagnosing dementia in Australia is that a GP will make an initial assessment and refer the patient to a memory clinic for further checking if necessary. The assessment is based on various information including medical history, neuroimaging, and neuropsychological tests. However, there are two major problems in making a decision with these data. One is that no intrinsic correspondence can easily be found between dementia and the data. The other is that the amount of data is huge, making a consistent and objective judgement problematic. We propose to solve these problems by using advanced deep learning (DL) algorithms. DL is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for dementia assessment where raw data are largely unlabelled and un-categorized. DL has recently been increasingly explored in artificial intelligence research. We will develop the algorithm based on identifying all the clinical data that are currently being used by GPs in the initial assessment of dementia. The experimental data will be acquired from ADNI and AIBL. |
| Additional Investigators |
