Web-based fully automated cephalometric analysis by deep learning. (October 2020) - Kim, Hannah; Shim, Eungjune; Park, Jungeun; Kim, Yoon-Ji; Lee, Uilyong; Kim, Youngjun
Highlights: Fully automated cephalometric analysis by deep learning was proposed. A web-based application with the proposed fully automated algorithm was developed. Large dataset was acquired by multiple institutes and used for training and testing. Landmark detection results were accurate compared with experts' results. Highly successful rates for anatomical type classification were demonstrated. Abstract: 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.