A 3D Deep Residual Convolutional Neural Network for Differential Diagnosis of Parkinsonian Syndromes on 18F-FDG PET Images

Conclusions: We successfully developed a 3D deep residual convolutional neural network for automated differential diagnosis of IPD and atypical parkinsonism with excellent performance in diagnostic accuracy. The greatest salience was in expected regions of the basal ganglia, but initial findings also implicate high order visual cortex and prefrontal cortex. The method is currently under detailed assessment in a separate group of several hundred parkinsonian patients, and with emphasis on the interpretation of the saliency maps in diagnosis. [1] Tang et al. Lancet Neuro 2010 [2] Wu et al. SNMMI 2018 [3] Springenberg, J.T., arXiv:1412.6806

Source: Journal of Nuclear Medicine - May 20, 2019 Category: Nuclear Medicine Authors: Zhao, Y., Wu, P., Wang, J., Navab, N., Yakushev, I., Weber, W., Schwaiger, M., Huang, S.-C., Cumming, P., Rominger, A., Zuo, C., Shi, K. Tags: Brain Imaging Reloaded I Source Type: research