A 3D dental model dataset with pre/post-orthodontic treatment for automatic tooth alignment.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-23 DOI:10.1038/s41597-024-04138-7
Shaofeng Wang, Changsong Lei, Yaqian Liang, Jun Sun, Xianju Xie, Yajie Wang, Feifei Zuo, Yuxin Bai, Song Li, Yong-Jin Liu
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Abstract

Traditional orthodontic treatment relies on subjective estimations of orthodontists and iterative communication with technicians to achieve desired tooth alignments. This process is time-consuming, complex, and highly dependent on the orthodontist's experience. With the development of artificial intelligence, there's a growing interest in leveraging deep learning methods to achieve tooth alignment automatically. However, the absence of publicly available datasets containing pre/post-orthodontic 3D dental models has impeded the advancement of intelligent orthodontic solutions. To address this limitation, this paper proposes the first public 3D orthodontic dental dataset, comprising 1,060 pairs of pre/post-treatment dental models sourced from 435 patients. The proposed dataset encompasses 3D dental models with diverse malocclusion, e.g., tooth crowding, deep overbite, and deep overjet; and comprehensive professional annotations, including tooth segmentation labels, tooth position information, and crown landmarks. We also present technical validations for tooth alignment and orthodontic effect evaluation. The proposed dataset is expected to contribute to improving the efficiency and quality of target tooth position design in clinical orthodontic treatment utilizing deep learning methods.

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用于自动牙齿排列的正畸治疗前后三维牙科模型数据集。
传统的正畸治疗依赖于正畸医生的主观估计以及与技术人员的反复沟通来实现理想的牙齿排列。这一过程耗时、复杂,而且高度依赖于正畸医生的经验。随着人工智能的发展,人们对利用深度学习方法自动实现牙齿排列越来越感兴趣。然而,由于缺乏包含正畸前后三维牙科模型的公开数据集,阻碍了智能正畸解决方案的发展。为了解决这一局限性,本文提出了首个公开的三维正畸牙科数据集,其中包括来自 435 名患者的 1,060 对治疗前/后牙科模型。该数据集包含各种错颌畸形的三维牙科模型,如牙齿拥挤、深咬合和深过咬合;以及全面的专业注释,包括牙齿分割标签、牙齿位置信息和牙冠地标。我们还介绍了牙齿排列和正畸效果评估的技术验证。预计该数据集将有助于提高利用深度学习方法在临床正畸治疗中设计目标牙齿位置的效率和质量。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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