Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning. (March 2022)

• Record Type: Journal Article
• Authors: Hao, J.; Liao, W.; Zhang, Y.L.; Peng, J.; Zhao, Z.; Chen, Z.; Zhou, B.W.; Feng, Y.; Fang, B.; Liu, Z.Z.; Zhao, Z.H.
• Is Part Of: Journal of dental research. Volume 101:Number 3(2022)
• Page Start: 304
• Page End: 311
• Publication Date: 2022-03
• Subjects: intraoral scan -- machine learning -- artificial intelligence -- medical imaging -- neural networks -- digital dentistry; Dentistry -- Periodicals; Dentistry -- Social aspects -- Periodicals; Dentistry -- Periodicals; Research -- Periodicals; 617.6005
• DOI: 10.1177/00220345211040459
• Languages: English
• ISSNs: 0022-0345

Abstract

Digital dentistry plays a pivotal role in dental health care. A critical step in many digital dental systems is to accurately delineate individual teeth and the gingiva in the 3-dimension intraoral scanned mesh data. However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. This article presents an accurate, efficient, and fully automated deep learning model trained on a data set of 4, 000 intraoral scanned data annotated by experienced human experts. On a holdout data set of 200 scans, our model achieves a per-face accuracy, average-area accuracy, and area under the receiver operating characteristic curve of 96.94%, 98.26%, and 0.9991, respectively, significantly outperforming the state-of-the-art baselines. In addition, our model takes only about 24 s to generate segmentation outputs, as opposed to >5 min by the baseline and 15 min by human experts. A clinical performance test of 500 patients with malocclusion and/or 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.