  Alzheimer's Disease (AD) is the most widespread neurodegenerative disease,
affecting over 50 million people across the world. While its progression cannot
be stopped, early and accurate diagnostic testing can drastically improve
quality of life in patients. Currently, only qualitative means of testing are
employed in the form of scoring performance on a battery of cognitive tests.
The inherent disadvantage of this method is that the burden of an accurate
diagnosis falls on the clinician's competence. Quantitative methods like MRI
scan assessment are inaccurate at best,due to the elusive nature of visually
observable changes in the brain. In lieu of these disadvantages to extant
methods of AD diagnosis, we have developed ADiag, a novel quantitative method
to diagnose AD through GraphSAGE Network and Dense Differentiable Pooling (DDP)
analysis of large graphs based on thickness difference between different
structural regions of the cortex. Preliminary tests of ADiag have revealed a
robust accuracy of 83%, vastly outperforming other qualitative and quantitative
diagnostic techniques.
