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

Abstract

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.