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: | Shivam Kumar |
| Institution: | Indian Institute of Technology, Roorkee, India |
| Department: | Electrical Engineering |
| Country: | |
| Proposed Analysis: | We are trying to implement a state-of-the-art deep multimodal learning algorithm to diagnose, classify and predict the prognostics of Alzheimer's Disease. We are hoping to train our model over neuroimaging, genetics, and clinical data. We have already achieved over 95% accuracy on stage detection, and we wish to improve further with our designed model with the advanced data set of ADNI. |
| Additional Investigators | |
| Investigator's Name: | Vikrant Dey |
| Proposed Analysis: | We are trying to implement a state-of-the-art deep multimodal learning algorithm to diagnose, classify and predict the prognostics of Alzheimer's Disease. We are hoping to train our model over neuroimaging, genetics, and clinical data. We have already achieved over 95% accuracy on stage detection, and we wish to improve further with our designed model with the advanced data set of ADNI. |
| Investigator's Name: | Nitish Aggarwal |
| Proposed Analysis: | We are trying to implement a state-of-the-art deep multimodal learning algorithm to diagnose, classify and predict the prognostics of Alzheimer's Disease. We are hoping to train our model over neuroimaging, genetics, and clinical data. We have already achieved over 95% accuracy on stage detection, and we wish to improve further with our designed model with the advanced data set of ADNI. |
| Investigator's Name: | Nalin Mathur |
| Proposed Analysis: | We are trying to implement a state-of-the-art deep multimodal learning algorithm to diagnose, classify and predict the prognostics of Alzheimer's Disease. We are hoping to train our model over neuroimaging, genetics, and clinical data. We have already achieved over 95% accuracy on stage detection, and we wish to improve further with our designed model with the advanced data set of ADNI. |
| Investigator's Name: | Dr. Gopi Nath Pillai |
| Proposed Analysis: | We are trying to implement a state-of-the-art deep multimodal learning algorithm to diagnose, classify and predict the prognostics of Alzheimer's Disease. We are hoping to train our model over neuroimaging, genetics, and clinical data. We have already achieved over 95% accuracy on stage detection, and we wish to improve further with our designed model with the advanced data set of ADNI. |
