Abstract

The differentiation of dementia with Lewy bodies (DLB) from Alzheimer’s disease (AD) using brain perfusion single photon emission tomography is important but is challenging because these conditions exhibit typical features. The cingulate island sign (CIS) is the most recently identified specific feature of DLB for a differential diagnosis. The current study aimed to examine the usefulness of deep-learning-based imaging classification for the diagnoses of DLB and AD. Furthermore, we investigated whether CIS was emphasized by a deep convolutional neural network (CNN) during differentiation. Brain perfusion single photon emission tomography images from 80 patients, each with DLB and AD, and 80 individuals with normal cognition (NL) were used for training and 20 each for final testing. The CNN was trained on brain surface perfusion images. Gradient-weighted class activation mapping (Grad-CAM) was applied to the CNN to visualize the features that was emphasized by the trained CNN. The binary classifications between DLB and NL, DLB and AD, and AD and NL were 93.1%, 89.3%, and 92.4% accurate, respectively. The CIS ratios closely correlated with the output scores before softmax for DLB–AD discrimination (DLB/AD scores). The Grad-CAM highlighted CIS in the DLB discrimination. Visualization of learning process by guided Grad-CAM revealed that CIS became more focused by the CNN as the training progressed. The DLB/AD score was significantly associated with the three core features of DLB. Deep-learning-based imaging classification was useful for an objective and accurate differentiation of DLB from AD and for predicting clinical features of DLB. The CIS was identified as a specific feature during DLB classification. The visualization of specific features and learning processes could be critical in deep learning to discover new imaging features.

Highlights

  • Neuroimaging has contributed to the classification of neurodegenerative dementias such as dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD)

  • Deep learning is a primary branch of artificial intelligence comprising a deep convolutional neural network (CNN) capable of automatic feature extraction from data, and recent advances in deep learning have remarkably improved the performance of image classification and detection[14,15]

  • The current study aims to investigate whether a trained CNN can identify the cingulate island sign (CIS), which is the most recently recognized imaging feature of DLB, while a deep two dimensional CNN (2D-CNN) objectively and automatically classifies brain surface perfusion images through the 3D-SSP of DLB, AD, and individuals with normal cognition (NL)

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Summary

Introduction

Neuroimaging has contributed to the classification of neurodegenerative dementias such as dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD). Disease-specific features have been extracted from brain perfusion single photon emission tomography (SPECT) images to assist with differential diagnoses of DLB and AD. An imaging feature for DLB discrimination is occipital hypoperfusion[4,5,6,7] Another finding that can produce a difference between DLB and AD is perfusion in the posterior cingulate cortex (PCC). A deep-learning-based SPECT interpretation system that can differentiate between DLB and AD has not been described. The current study aims to investigate whether a trained CNN can identify the CIS, which is the most recently recognized imaging feature of DLB, while a deep two dimensional CNN (2D-CNN) objectively and automatically classifies brain surface perfusion images through the 3D-SSP of DLB, AD, and individuals with normal cognition (NL).

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