Achararit Paniti, Manaspon Chawan, Jongwannasiri Chavin, Kulthanaamondhita Promphakkon, Itthichaisri Chumpot, Chantarangsu Soranun, Osathanon Thanaphum, Phattarataratip Ekarat, Sappayatosok Kraisorn
Princess Srisavangavadhana Faculty of Medicine, Chulabhorn Royal Academy, Bangkok, 10210, Thailand.
Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand.
BMC Oral Health. 2025 Jan 28;25(1):152. doi: 10.1186/s12903-025-05425-4.
Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.
The dataset included cropped panoramic radiographs of 1200 ILTMs. The parameters examined were ILTM angulation, class, and position. The radiographs were randomly split into test datasets, while the remaining images were utilized for training and validation. Data augmentation techniques were applied. Another set of radiographs was used to compare the accuracy between human experts and the top-performing CNN. This dataset was also given to DSs and GPs. The participants were instructed to classify the parameters of the ILTMs both with and without the aid of the best-performing CNN model. The results, as well as the Pederson DI and time taken for both groups with and without CNN assistance, were statistically analyzed.
All the selected CNN models successfully classified ILTM angulation, class, and position. Within the DS and GP groups, the accuracy and kappa scores were significantly greater when CNN assistance was used. Among the groups, performance tests without CNN assistance revealed no significant differences in any category. However, compared with DSs, GPs took significantly less time for the class and total time, a trend that persisted when CNN assistance was used. With the CNN, the GPs achieved significantly higher accuracy and kappa scores for class classification than the DSs did (p = 0.035 and 0.010). Conversely, the DS group, with the CNN, exhibited higher accuracy and kappa scores for position classification than did the GP group (p < 0.001).
The CNN can achieve accuracies ranging from 87 to 96% for ILTM classification. With the assistance of the CNN, both DSs and GPs exhibited significantly higher accuracy in ILTM classification. Additionally, compared with DSs with and without CNN assistance, GPs took significantly less time to inspect the class and overall.
评估下颌阻生第三磨牙（ILTM）手术拔除的难度对于预测术后并发症和估计手术时长至关重要。本研究的目的是评估卷积神经网络（CNN）在确定ILTM的角度、位置、分类和难度指数（DI）方面的有效性。此外，我们比较了这些参数以及深度学习（DL）模型、六年级牙科学生（DSs）和普通牙科医生（GPs）在有无CNN辅助情况下的解读所需时间。
数据集包括1200例ILTM的裁剪全景X线片。所检查的参数为ILTM的角度、分类和位置。X线片被随机分为测试数据集，其余图像用于训练和验证。应用了数据增强技术。另一组X线片用于比较人类专家与表现最佳的CNN之间的准确性。该数据集也提供给了DSs和GPs。参与者被要求在有无表现最佳的CNN模型辅助下对ILTM的参数进行分类。对结果以及有和没有CNN辅助的两组的Pederson DI和所需时间进行了统计分析。
所有选定的CNN模型都成功对ILTM的角度、分类和位置进行了分类。在DS组和GP组中，使用CNN辅助时的准确性和kappa评分显著更高。在各小组之间，无CNN辅助的性能测试在任何类别中均未显示出显著差异。然而，与DSs相比，GPs对分类和总时间的用时显著更少，在使用CNN辅助时这一趋势仍然存在。使用CNN时，GPs在分类方面的准确性和kappa评分显著高于DSs（p = 0.035和0.010）。相反，使用CNN时，DS组在位置分类方面的准确性和kappa评分高于GP组（p < 0.001）。
CNN对ILTM分类的准确率可达87%至96%。在CNN辅助下，DSs和GPs在ILTM分类方面的准确性均显著更高。此外，与有和没有CNN辅助的DSs相比，GPs检查分类和整体情况所用时间显著更少。
