Clinically Applicable System For 3D Teeth Segmentation in Intraoral Scans using Deep Learning

Abstract

Abstract Digital dentistry plays a pivotal role in dental healthcare. A critical step in many digital dental systems is to accurately delineate individual teeth and the gingiva in the three-dimension (3D) intraoral scanned (IOS) mesh data. However, previous state-of-the-art methods are either time-consuming or error-prone, hence hinder their clinical applicability. In this paper, we present an accurate, efficient, and fully-automated deep learning model, trained on a dataset of 4,000 IOS data annotated by experienced human experts. On a hold-out dataset of 200 scans, our model achieves a per-face accuracy, average-area accuracy and area under the receiver operating characteristic curve (AUC) of 96.94%, 98.26%, and 0.9991, respectively, significantly outperforming the state-of-the-art baseline. In addition, our model only takes about 24 seconds to generate segmentation outputs, as compared to over 5 minutes by the baseline and 15 minutes by human experts. A clinical performance test of 500 patients with malocclusion or/and abnormal teeth shows that 96.9% of the segmentations are satisfactory for clinical applications, 2.9% automatically trigger alarms for human improvement, and only 0.2% of them need rework. Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry.