Deep convolutional neural networks for automated scoring of pentagon copying test results
Jumpei Maruta, Kentaro Uchida, Hideo Kurozumi, Satoshi Nogi, Satoshi Akada, Aki Nakanishi, Miki Shinoda, Masatsugu Shiba, Koki Inoue
Abstract
This study aims to investigate the accuracy of a fine-tuned deep convolutional neural network (CNN) for evaluating responses to the pentagon copying test (PCT). To develop a CNN that could classify PCT images, we fine-tuned and compared the pre-trained CNNs (GoogLeNet, VGG-16, ResNet-50, Inception-v3). To collate our training dataset, we collected 1006 correct PCT images and 758 incorrect PCT images drawn on a test sheet by dementia suspected patients at the Osaka City Kosaiin Hospital between April 2009 and December 2012. For a validation dataset, we collected PCT images from consecutive patients treated at the facility in April 2020. We examined the ability of the CNN to detect correct PCT images using a validation dataset. For a validation dataset, we collected PCT images (correct, 41; incorrect, 16) from 57 patients. In the validation testing for an ability to detect correct PCT images, the fine-tuned GoogLeNet CNN achieved an area under the receiver operating characteristic curve of 0.931 (95% confidence interval 0.853-1.000). These findings indicate that our fine-tuned CNN is a useful method for automatically evaluating PCT images. The use of CNN-based automatic scoring of PCT can potentially reduce the burden on assessors in screening for dementia.