用深度学习模型自动判断上颌恒牙埋伏风险，减少医生判断差异。
Development of a fully deep learning model to improve the reproducibility of sector classification systems for predicting unerupted maxillary canine likelihood of impaction
- 基于1222张影像训练AI模型，自动分类埋伏牙位置。
- 最佳模型准确率达76.8%，3区分类法最稳定（组内一致率0.80~0.92）。
- 适用于正畸与牙科临床，降低医生间判断偏差。
目的：开发全深度学习模型，降低不同医生对未萌上颌恒牙埋伏风险评估中使用的分区分类系统的判断差异。方法：三位正畸医生和三位普通牙医对306张头颅侧位片（T0）中的未萌上颌恒牙位置，分别按5区、4区、3区分类系统进行评估，并在四周后重复评估（T1）。使用Cohen's K和Fleiss K评估组内与组间一致性，采用z检验比较组间差异。同一组影像输入预训练于1,222张影像的AI模型，根据敏感性和精确度选出最优模型。结果：3区分类法具有最高可重复性，各医师前后评估一致性（Cohen's K）为0.80至0.92，总体一致性达0.85（95%置信区间0.83–0.87）；整体组间一致性（Fleiss K）为0.69至0.70。教育背景对一致性无显著影响（p>0.05）。DenseNet121为表现最佳模型，在三类分类中总体准确率为76.8%。结论：可通过深度学习模型实现未萌上颌恒牙位置的自动分类，提升评估一致性。
原文摘要 · Abstract (English)
Objectives. The aim of the present study was to develop a fully deep learning model to reduce the intra- and inter-operator reproducibility of sector classification systems for predicting unerupted maxillary canine likelihood of impaction. Methods. Three orthodontists (Os) and three general dental practitioners (GDPs) classified the position of unerupted maxillary canines on 306 radiographs (T0) according to the three different sector classification systems (5-, 4-, and 3-sector classification system). The assessment was repeated after four weeks (T1). Intra- and inter-observer agreement were evaluated with Cohen's K and Fleiss K, and between group differences with a z-test. The same radiographs were tested on different artificial intelligence (AI) models, pre-trained on an extended dataset of 1,222 radiographs. The best-performing model was identified based on its sensitivity and precision. Results. The 3-sector system was found to be the classification method with highest reproducibility, with an agreement (Cohen's K values) between observations (T0 versus T1) for each examiner ranged from 0.80 to 0.92, and an overall agreement of 0.85 [95% confidence interval (CI) = 0.83-0.87]. The overall inter-observer agreement (Fleiss K) ranged from 0.69 to 0.7. The educational background did not affect either intra- or inter-observer agreement (p>0.05). DenseNet121 proved to be the best-performing model in allocating impacted canines in the three different classes, with an overall accuracy of 76.8%. Conclusion. AI models can be designed to automatically classify the position of unerupted maxillary canines.
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