Çelik Berrin, Genç Mehmet Zahid, Çelik Mahmut Emin
Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara Yıldırım Beyazıt University, Ankara, Turkey.
Department of Electrical Electronics Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey.
Oral Radiol. 2025 Jan;41(1):102-110. doi: 10.1007/s11282-024-00781-3. Epub 2024 Oct 27.
This work proposes a novel method to evaluate root canal filling (RCF) success using artificial intelligence (AI) and image analysis techniques.
1121 teeth with root canal treatment in 597 periapical radiographs (PARs) were anonymized and manually labeled. First, RCFs were segmented using 5 different state-of-the-art deep learning models based on convolutional neural networks. Their performances were compared based on the intersection over union (IoU), dice score and accuracy. Additionally, fivefold cross validation was applied for the best-performing model and their outputs were later used for further analysis. Secondly, images were processed via a graphical user interface (GUI) that allows dental clinicians to mark the apex of the tooth, which was used to find the distance between the apex of the tooth and the nearest RCF prediction of the deep learning model towards it. The distance can show whether the RCF is normal, short or long.
Model performances were evaluated by well-known evaluation metrics for segmentation such as IoU, Dice score and accuracy. CNN-based models can achieve an accuracy of 88%, an IoU of 79% and Dice score of 88% in segmenting root canal fillings.
Our study demonstrates that AI-based solutions present accurate and reliable performance for root canal filling evaluation.
本研究提出一种利用人工智能（AI）和图像分析技术评估根管充填（RCF）成功率的新方法。
对597张根尖片（PARs）中1121颗接受根管治疗的牙齿进行匿名处理并手动标注。首先，使用5种基于卷积神经网络的不同的先进深度学习模型对根管充填物进行分割。根据交并比（IoU）、骰子系数和准确率对它们的性能进行比较。此外，对性能最佳的模型应用五折交叉验证，其输出结果随后用于进一步分析。其次，通过图形用户界面（GUI）对图像进行处理，该界面允许牙科临床医生标记牙齿的根尖，用于确定牙齿根尖与深度学习模型对其最近的根管充填预测之间的距离。该距离可以显示根管充填是正常、过短还是过长。
通过交并比（IoU）、骰子系数和准确率等用于分割的知名评估指标对模型性能进行评估。基于卷积神经网络的模型在根管充填物分割中可达到88%的准确率、79%的交并比和88%的骰子系数。
我们的研究表明，基于人工智能的解决方案在根管充填评估中表现出准确可靠的性能。
