Evaluation of root canal filling length on periapical radiograph using artificial intelligence.

IF 1.6 3区 医学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Berrin Çelik, Mehmet Zahid Genç, Mahmut Emin Çelik
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引用次数: 0

Abstract

Objectives: This work proposes a novel method to evaluate root canal filling (RCF) success using artificial intelligence (AI) and image analysis techniques.

Methods: 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.

Results: 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.

Conclusions: Our study demonstrates that AI-based solutions present accurate and reliable performance for root canal filling evaluation.

利用人工智能评估根尖周X光片上的根管充填长度。
目的:方法:对 597 张根尖周 X 光片(PAR)中 1121 颗接受过根管治疗的牙齿进行匿名和人工标记。首先,使用基于卷积神经网络的 5 种不同的先进深度学习模型对 RCF 进行分割。根据交集大于联合(IoU)、骰子得分和准确率对它们的性能进行了比较。此外,还对表现最佳的模型进行了五倍交叉验证,随后将其输出用于进一步分析。其次,通过图形用户界面(GUI)处理图像,允许牙科临床医生标记牙齿的顶点,用来找出牙齿顶点与深度学习模型对其最近的 RCF 预测之间的距离。该距离可显示 RCF 是正常、短还是长:通过 IoU、Dice 分数和准确率等众所周知的分割评价指标对模型性能进行了评估。基于 CNN 的模型在分割根管填充物时,准确率达到 88%,IoU 达到 79%,Dice 分数达到 88%:我们的研究表明,基于人工智能的解决方案可为根管充填评估提供准确可靠的性能。
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来源期刊
Oral Radiology
Oral Radiology DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.20
自引率
13.60%
发文量
87
审稿时长
>12 weeks
期刊介绍: As the official English-language journal of the Japanese Society for Oral and Maxillofacial Radiology and the Asian Academy of Oral and Maxillofacial Radiology, Oral Radiology is intended to be a forum for international collaboration in head and neck diagnostic imaging and all related fields. Oral Radiology features cutting-edge research papers, review articles, case reports, and technical notes from both the clinical and experimental fields. As membership in the Society is not a prerequisite, contributions are welcome from researchers and clinicians worldwide.
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