Affiliations: [a]
Department of Otolaryngology-Head and Neck Surgery, Bill Wilkerson Center, Vanderbilt University Medical Center, Nashville, TN, USA
| [b]
State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| [c]
Guangdong Yunjian Intelligent Technology Co. Ltd., Foshan, Guangdong, China
Correspondence:
[*]
Correspondence to: Yike Li, 1161 21st Avenue South, S2211 Medical Center North, Nashville, TN 37075, USA. Tel.: +1 615 343 8146; E-mail: yike.li.1@vumc.org and Jiajie Guo, RM B210, Advanced Manufacturing Bldg East, Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan, Hubei, China. Tel.: +86 027 87559416; E-mail: jiajie.guo@hust.edu.cn.
Abstract: Background:The Pentagon Drawing Test (PDT) is a common assessment for visuospatial function. Evaluating the PDT by artificial intelligence can improve efficiency and reliability in the big data era. This study aimed to develop a deep learning (DL) framework for automatic scoring of the PDT based on image data. Methods:A total of 823 PDT photos were retrospectively collected and preprocessed into black-and-white, square-shape images. Stratified fivefold cross-validation was applied for training and testing. Two strategies based on convolutional neural networks were compared. The first strategy was to perform an image classification task using supervised transfer learning. The second strategy was designed with an object detection model for recognizing the geometric shapes in the figure, followed by a predetermined algorithm to score based on their classes and positions. Results:On average, the first framework demonstrated 62%accuracy, 62%recall, 65%precision, 63%specificity, and 0.72 area under the receiver operating characteristic curve. This performance was substantially outperformed by the second framework, with averages of 94%, 95%, 93%, 93%, and 0.95, respectively. Conclusion:An image-based DL framework based on the object detection approach may be clinically applicable for automatic scoring of the PDT with high efficiency and reliability. With a limited sample size, transfer learning should be used with caution if the new images are distinct from the previous training data. Partitioning the problem-solving workflow into multiple simple tasks should facilitate model selection, improve performance, and allow comprehensible logic of the DL framework.
Keywords: Computer vision, convolutional neural network, deep learning, image classification, object detection, Pentagon Drawing Test
