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: | Oliver Faust |
| Institution: | Sheffield Hallam University |
| Department: | Engineering |
| Country: | |
| Proposed Analysis: | We want to automate the detection of cerebral microbleeds. That automatization will be established through artificial intelligence algorithms which analyse the MRI images from the ADNI database. The automatization problem was posed by Su Li who worked on manual detection of cerebral microbleeds [1]. As such, manual detection of cerebral microbleeds is affected by intra- and inter-observer variability. This problem is significant, because this variability limits the microbleeds detection quality. We plan to support the detection of cerebral microbleeds by establishing regions of interest through heatmaps in the MRI images. These regions of interest will indicate likely locations of cerebral microbleeds. For the methodology, we plan to follow and indeed collaborate with Yildirim et al. who used artificial intelligence algorithms to create a heatmap for kidney stone detection [2]. References: [1] Stefaniak, J. D., Su, L., Mak, E., Sheikh‐Bahaei, N., Wells, K., Ritchie, K., ... & O'brien, J. T. (2018). Cerebral small vessel disease in middle age and genetic predisposition to late‐onset Alzheimer's disease. Alzheimer's & Dementia, 14(2), 253-258. [2] Yildirim, K., Bozdag, P. G., Talo, M., Yildirim, O., Karabatak, M., & Acharya, U. R. (2021). Deep Learning Model for Automated Kidney Stone Detection using Coronal CT Images. Computers in Biology and Medicine, 104569. |
| Additional Investigators |
