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: | YIYANG WANG |
| Institution: | DePaul Medical Informatics Lab |
| Department: | College of Computing and Digital Media |
| Country: | |
| Proposed Analysis: | We are working on a similar problem but with a different eye disease.We want to develop a predictive model that can also work on your data. Advanced form of age-related macular degeneration (AMD) is a major health burden that can lead to irreversible vision loss in the elderly population. For early preventative interventions, there is a lack of effective tools to predict the prognosis outcome of advanced AMD because of the similar visual appearance of retinal image scans in the early stage and the variability of prognosis paths among patients. The existing prognosis models have several limitations: First, previous studies assume constant time intervals between doctor visits; however, in real world clinical settings, the visits may happen at irregular time intervals. The assumption of constant time intervals will lead to over-optimistic prediction results on specific training data sets while failing to produce generalizable results on new patient data sets. Second, current studies only predict one form of advanced AMD form at a time. Third, computer-based prognosis results are typically not validated on new patients and therefore, it is difficult to evaluate the generalizability of the proposed approaches. Lastly, there is a lack of interpretability of the models and explainability of how a computer-based prognosis determination has been made. The overall objective for this project is to design, develop, and evaluate AMD prognosis prediction models that can detect most relevant images containing AMD biomarkers, manage unevenly spaced sequential optical coherence tomography (OCT) images and predict all advanced AMD forms that can help with the interpretation and explainability of computer-aided prognosis models. |
| Additional Investigators | |
| Investigator's Name: | Daniela Raicu |
| Proposed Analysis: | Advanced form of age-related macular degeneration (AMD) is a major health burden that can lead to irreversible vision loss in the elderly population. For early preventative interventions, there is a lack of effective tools to predict the prognosis outcome of advanced AMD because of the similar visual appearance of retinal image scans in the early stage and the variability of prognosis paths among patients. The existing prognosis models have several limitations: First, previous studies assume constant time intervals between doctor visits; however, in real world clinical settings, the visits may happen at irregular time intervals. The assumption of constant time intervals will lead to over-optimistic prediction results on specific training data sets while failing to produce generalizable results on new patient data sets. Second, current studies only predict one form of advanced AMD form at a time. Third, computer-based prognosis results are typically not validated on new patients and therefore, it is difficult to evaluate the generalizability of the proposed approaches. Lastly, there is a lack of interpretability of the models and explainability of how a computer-based prognosis determination has been made. The overall objective for this project is to design, develop, and evaluate AMD prognosis prediction models that can detect most relevant images containing AMD biomarkers, manage unevenly spaced sequential optical coherence tomography (OCT) images and predict all advanced AMD forms that can help with the interpretation and explainability of computer-aided prognosis models. |
