Mild cognitive impairment (MCI) has been described as the intermediary stage before Alzheimer's Disease - many people however remain stable or even demonstrate improvement in cognition. Early detection of progressive MCI (pMCI) therefore can be utilised in identifying at-risk individuals and directing additional medical treatment in order to revert conversion to AD as well as provide psychosocial support for the person and their family. This paper presents a novel solution in the early detection of pMCI people and classification of AD risk within MCI people. We proposed a model, MudNet, to utilise deep learning in the simultaneous prediction of progressive/stable MCI classes and time-to-AD conversion where high-risk pMCI people see conversion to AD within 24 months and low-risk people greater than 24 months. MudNet is trained and validated using baseline clinical and volumetric MRI data (n = 559 scans) from participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The model utilises T1-weighted structural MRIs alongside clinical data which also contains neuropsychological (RAVLT, ADAS-11, ADAS-13, ADASQ4, MMSE) tests as inputs. The averaged results of our model indicate a binary accuracy of 69.8% for conversion predictions and a categorical accuracy of 66.9% for risk classifications.
|Title of host publication||2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)|
|Publication status||Accepted/In press - 23 Oct 2020|
|Event||7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies 2020 - online, Leicester , United Kingdom|
Duration: 7 Dec 2020 → 10 Dec 2020
|Other||7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies 2020|
|Abbreviated title||BDCAT 2020|
|Period||7/12/20 → 10/12/20|