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: | Tianling Hou |
| Institution: | University of Pittsburgh |
| Department: | School of Pharmacy |
| Country: | |
| Proposed Analysis: | We are interested in developing an AI-based platform for the prediction of Alzheimer’s disease (AD). Recent research studies are mostly focused on single modality, especially neuroimaging data. Our goal is to fully explore the whole ADNI dataset (in a multimodal setting) and develop different machine learning and deep learning methods to generate interpretable, accurate AD prediction models. Our specific goals: 1. Identify informative and important AD features from the dataset 2. Develop machine learning models for the early detection of AD 3. Develop machine learning models for the AD progression prediction in a detailed setting (e.g. within 3, 5, 10 years from the baseline) 4. Leveraging knowledge from the developed machine learning model and using transfer learning to train new models for improved AD prediction on local hospitals 5. Develop an AI-based platform that helps physicians with the early diagnosis of AD |
| Additional Investigators | |
| Investigator's Name: | Xiangqun Xie |
| Proposed Analysis: | We are interested in developing an AI-based platform for the prediction of Alzheimer’s disease (AD). Recent research studies are mostly focused on single modality, especially neuroimaging data. Our goal is to fully explore the whole ADNI dataset (in a multimodal setting) and develop different machine learning and deep learning methods to generate interpretable, accurate AD prediction models. Our specific goals: 1. Identify informative and important AD features from the dataset 2. Develop machine learning models for the early detection of AD 3. Develop machine learning models for the AD progression prediction in a detailed setting (e.g. within 3, 5, 10 years from the baseline) 4. Leveraging knowledge from the developed machine learning model and using transfer learning to train new models for improved AD prediction on local hospitals 5. Develop an AI-based platform that helps physicians with the early diagnosis of AD |
| Investigator's Name: | Guangyi Zhao |
| Proposed Analysis: | We are interested in developing an AI-based platform for the prediction of Alzheimer’s disease (AD). Recent research studies are mostly focused on single modality, especially neuroimaging data. Our goal is to fully explore the whole ADNI dataset (in a multimodal setting) and develop different machine learning and deep learning methods to generate interpretable, accurate AD prediction models. Our specific goals: 1. Identify informative and important AD features from the dataset 2. Develop machine learning models for the early detection of AD 3. Develop machine learning models for the AD progression prediction in a detailed setting (e.g. within 3, 5, 10 years from the baseline) 4. Leveraging knowledge from the developed machine learning model and using transfer learning to train new models for improved AD prediction on local hospitals 5. Develop an AI-based platform that helps physicians with the early diagnosis of AD |
