用几何深度学习自动标记唇腭裂新生儿3D牙弓关键点，精度媲美专家。
Geometric Deep Learning for Automated Landmarking of Maxillary Arches on 3D Oral Scans from Newborns with Cleft Lip and Palate
- 基于几何深度学习构建自动化地标标注流程
- 在100例新生儿模型上达到94.44%准确率，误差仅1.676±0.959mm
- 适合需要高效量化牙弓形态的临床与科研人员
3D扫描技术的快速发展推动了牙科石膏模型的批量数字化，但临床与研究中仍普遍依赖人工测量方法进行形态学分析，如关键点标记。这一过程对颅颌面疾病治疗规划至关重要。本研究旨在开发并验证一种几何深度学习模型，能够在无需大量训练数据的情况下，对复杂且特殊的患者群体——新生儿唇腭裂患者，实现与人类专家相当的精准、可靠的地标标注。所提出的流程在100个来自新生儿唇腭裂患者的模型上表现出94.44%的准确率，绝对平均误差为1.676 ± 0.959 mm。该方法具有作为快速、准确、可靠量化上颌弓形态特征工具的潜力，并可成为未来全自动牙科治疗流程中的关键环节。
原文摘要 · Abstract (English)
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.
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