Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning

Abstract

This study evaluates the accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. We developed a dynamic graph convolutional neural network (DGCNN)-based algorithm for automatic tooth segmentation and classification using 516 digital dental models. We segmented 30 digital dental models using three methods for comparison: (1) automatic tooth segmentation (AS) using the DGCNN-based algorithm from LaonSetup software, (2) landmark-based tooth segmentation (LS) using OrthoAnalyzer software, and (3) tooth designation and segmentation (DS) using Autolign software. We evaluated the segmentation success rate, mesiodistal (MD) width, clinical crown height (CCH), and segmentation time. For the AS, LS, and DS, the tooth segmentation success rates were 97.26%, 97.14%, and 87.86%, respectively (p < 0.001, post-hoc; AS, LS > DS), the means of MD widths were 8.51, 8.28, and 8.63 mm, respectively (p < 0.001, post hoc; DS > AS > LS), the means of CCHs were 7.58, 7.65, and 7.52 mm, respectively (p < 0.001, post-hoc; LS > DS, AS), and the means of segmentation times were 57.73, 424.17, and 150.73 s, respectively (p < 0.001, post-hoc; AS < DS < LS). Automatic tooth segmentation of a digital dental model using deep learning showed high segmentation success rate, accuracy, and efficiency; thus, it can be used for orthodontic diagnosis and appliance fabrication.