PDF(Pubmed)
Abstract：
With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist\'s task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.
摘要：
随着口腔正畸数字化程度的提高,某些正畸制造工艺，如间接粘合托盘的制造，对准器生产，或电线弯曲可以自动化。然而,正畸治疗计划和评估仍然是专家的任务和责任。由于正畸患者的生长和对正畸治疗的反应的预测本质上是复杂的和个体的，正畸医生利用从纵向收集的特征，多模态,和标准化的正畸数据集。目前,这些数据集由正畸医生使用，基于规则的治疗决策。在研究中,人工智能（AI）已成功应用于帮助正畸医生从此类数据集中提取相关数据。这里,人工智能已被用于临床图像的分析，例如在侧位头颅造影中的自动标志检测，也用于评估口内扫描或摄影数据。此外，AI用于帮助正畸医生为治疗决策提供决策支持，例如需要正颌手术或正畸拔牙。目前正畸人工智能研究的一个主要挑战是泛化性有限，因为大多数研究使用具有高偏倚风险的单中心数据。此外，比较不同研究和任务的人工智能几乎是不可能的，因为结果和结果指标差异很大，和基础数据集没有标准化。值得注意的是,只有少数人工智能在口腔正畸中的应用达到了完全的临床成熟和监管部门的批准，该领域的研究人员的任务是在正畸工作流程中处理真实世界的评估和实施AI。
