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: | Pragati Khandelwal |
| Institution: | Medi-Caps University |
| Department: | Computer Science Engineering |
| Country: | |
| Proposed Analysis: | Title: Machine Learning-based Identification of Early Biomarkers for Alzheimer's Disease using MRI Scans Objective: The primary objective of this project is to develop a machine learning algorithm that can identify early biomarkers of Alzheimer's disease using MRI scan data. The algorithm will be trained using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and will aim to accurately classify individuals into Alzheimer's disease, mild cognitive impairment, or normal cognitive function groups. Methodology: The project will involve pre-processing the MRI scan data to normalize the images and extract relevant features such as brain volume, gray matter density, and cortical thickness. The extracted features will then be used to train and evaluate various machine learning models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests. The performance of the models will be assessed using accuracy, sensitivity, and specificity metrics. The identified biomarkers will be further analyzed to explore their relationship with Alzheimer's disease progression. Expected Outcomes: The expected outcomes of this project include the development of a machine learning algorithm that can accurately identify early biomarkers of Alzheimer's disease using MRI scan data. The identified biomarkers will provide insight into the underlying mechanisms of the disease and could be used for early diagnosis and intervention. Conclusion: This project has the potential to contribute significantly to the field of Alzheimer's disease research by identifying new biomarkers that can aid in early detection and intervention. The findings could ultimately lead to improved patient outcomes and enhance the quality of life of individuals with Alzheimer's disease. |
| Additional Investigators |
