标题： 基于几何深度学习的唇腭裂新生儿三维口腔扫描上上颌弓自动标志点定位 显示英文标题

标题： Geometric Deep Learning for Automated Landmarking of Maxillary Arches on 3D Oral Scans from Newborns with Cleft Lip and Palate

摘要： 三维模型扫描的快速进步使得牙科粘土模型的大规模数字化成为可能。 然而，大多数临床医生和研究人员仍然在这些模型上使用手动测量分析方法，例如定位标志点。 这是颅颌面状况治疗计划中的重要步骤。 我们的目标是开发并测试一种几何深度学习模型，该模型能够在复杂且专门的患者群体——婴儿中，以与人类专家相当的准确度和可靠性标记标志点，而无需大量训练数据。 我们开发的流程在一组100个来自唇腭裂新生儿的模型上表现出94.44%的准确率，平均绝对误差为1.676 +/- 0.959毫米。 我们提出的流程有望作为一种快速、准确和可靠的上颌弓测量特征量化工具，同时也是未来完全自动化的牙科治疗流程的重要一步。 显示英文摘要

摘要： Rapid advances in 3D model scanning have enabled the mass digitization of dental clay models. However, most clinicians and researchers continue to use manual morphometric analysis methods on these models such as landmarking. This is a significant step in treatment planning for craniomaxillofacial conditions. We aimed to develop and test a geometric deep learning model that would accurately and reliably label landmarks on a complicated and specialized patient population -- infants, as accurately as a human specialist without a large amount of training data. Our developed pipeline demonstrated an accuracy of 94.44% with an absolute mean error of 1.676 +/- 0.959 mm on a set of 100 models acquired from newborn babies with cleft lip and palate. Our proposed pipeline has the potential to serve as a fast, accurate, and reliable quantifier of maxillary arch morphometric features, as well as an integral step towards a future fully automated dental treatment pipeline.