[Accuracy of Classification of Cerebral Blood Flow Reduction Patterns Using Statistical Analysis Images Generated with Simulated SPECT Datasets via Deep Learning].

Purpose: The aim of this study was to evaluate the classification accuracy of specific blood flow reduction patterns in clinical images by deep learning using simulation data.

Methods: We obtained Z-score maps for 100 cases each of simulated Alzheimer's disease (AD), simulated dementia with Lewy bodies (DLB), and simulated normal cognition (NC) by performing statistical analysis of the simulation data that provided defects and healthy patient data. The clinical images were determined by reference to radiological reports, and Z-score maps of AD (n=33), DLB (n=20), and NC (n=28) were used.