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.

中文翻译：

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正畸中的人工智能：批判性评论

随着正畸数字化程度的提高，某些正畸制造工艺（例如间接粘合托盘的制造、对准器生产或钢丝弯曲）可以实现自动化。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预测和对正畸治疗的反应本质上是复杂且个体化的，正畸医生利用从纵向、多模式和标准化正畸数据集中收集的特征。目前，正牙医生使用这些数据集来做出明智的、基于规则的治疗决策。在研究中，人工智能（AI）已成功应用于协助正畸医生从此类数据集中提取相关数据。在这里，人工智能已应用于临床图像分析，例如头颅侧位片中的自动地标检测，也可用于口腔内扫描或摄影数据的评估。此外，人工智能还可用于帮助正畸医生为治疗决策提供决策支持，例如是否需要正颌手术或正畸拔牙。当前正畸人工智能研究的一个主要挑战是普遍性有限，因为大多数研究使用具有高偏倚风险的单中心数据。此外，在不同的研究和任务中比较人工智能几乎是不可能的，因为结果和结果指标差异很大，而且基础数据集也没有标准化。值得注意的是，只有少数人工智能在正畸领域的应用达到了完全的临床成熟度和监管部门的批准，该领域的研究人员的任务是在正畸工作流程中处理人工智能的实际评估和实施。