1. Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning.
- Author
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Hao, J., Liao, W., Zhang, Y.L., Peng, J., Zhao, Z., Chen, Z., Zhou, B.W., Feng, Y., Fang, B., Liu, Z.Z., and Zhao, Z.H.
- Subjects
DEEP learning ,DENTISTRY ,MALOCCLUSION ,DIGITAL technology ,AUTOMATION - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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