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Web-based fully automated cephalometric analysis by deep learning
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DOI：10.1016/j.cmpb.2020.105513.png)
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Background and Objective: An accurate lateral cephalometric analysis is vital in orthodontic diagnosis. Identification of anatomic landmarks on lateral cephalograms is tedious, and errors may occur depending on the doctor's experience. Several attempts have been made to reduce this time-consuming process by automating the process through machine learning; however, they only dealt with a small amount of data from one institute. This study aims to develop a fully automated cephalometric analysis method using deep learning and a corresponding web-based application that can be used without high-specification hardware. Methods: We built our own dataset comprising 2,075 lateral cephalograms and ground truth positions of 23 landmarks from two institutes and trained a two-stage automated algorithm with a stacked hourglass deep learning model specialized for detecting landmarks in images. Additionally, a web-based application with the proposed algorithm for fully automated cephalometric analysis was developed for better accessibility regardless of the user's computer hardware, which is essential for a deep learning-based method. Results: The algorithm was evaluated with datasets from various devices and institutes, including a widely used open dataset and achieved 1.37 +/- 1.79 mm of point-to-point errors with ground truth positions for 23 cephalometric landmarks. Based on the predicted positions, anatomical types of the subjects were automatically classified and compared with the ground truth, and the automated algorithm achieved a successful classification rate of 88.43%. Conclusions: We expect that this fully automated cephalometric analysis algorithm and the web-based application can be widely used in various medical environments to save time and effort for manual marking and diagnosis. (c) 2020 Elsevier B.V. All rights reserved.
Keyword:
Fully automated cephalometry
Automated landmark detection
Web-based application
Deep learning
Stacked hourglass network
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