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Alzheimer's Disease (AD) is the most widespread neurodegenerative disease,affecting over 50 million people across the world. While its progression cannotbe stopped, early and accurate diagnostic testing can drastically improvequality of life in patients. Currently, only qualitative means of testing areemployed in the form of scoring performance on a battery of cognitive tests.The inherent disadvantage of this method is that the burden of an accuratediagnosis falls on the clinician's competence. Quantitative methods like MRIscan assessment are inaccurate at best,due to the elusive nature of visuallyobservable changes in the brain. In lieu of these disadvantages to extantmethods of AD diagnosis, we have developed ADiag, a novel quantitative methodto diagnose AD through GraphSAGE Network and Dense Differentiable Pooling (DDP)analysis of large graphs based on thickness difference between differentstructural regions of the cortex. Preliminary tests of ADiag have revealed arobust accuracy of 83%, vastly outperforming other qualitative and quantitativediagnostic techniques.

ADiag: Graph Neural Network Based Diagnosis of Alzheimer's Disease