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: | Prashnna Gyawali |
| Institution: | West Virginia University |
| Department: | LCSEE |
| Country: | |
| Proposed Analysis: | Our aim is to develop an explainable multimodal AI system for Alzheimer's disease diagnosis. The system will leverage multiple data sources, including neuroimaging scans, cognitive assessments, and demographic information. By combining information from different modalities, the model can capture a more comprehensive view of the disease and improve diagnostic accuracy. This proposal also identifies the lack of interpretability in the current AI models, posing a significant challenge in clinical applications, where transparency and trust are paramount. To address the interpretability challenge, we will employ state-of-the-art explainable AI techniques. The proposed model will provide not only accurate predictions but also explanations for its decisions, enabling clinicians and patients to understand the reasoning behind the diagnostic outcome. This transparency will enhance trust in the AI system and facilitate better collaboration between clinicians and AI algorithms. |
| Additional Investigators |
