ArticleOral radiology2025
Evaluation of root canal filling length on periapical radiograph using artificial intelligence.
Article in Oral radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence in periodontal disease research: a bibliometric and visualized analysis of global research trends (2007-2025).The Saudi dental journal · 2026Review
- Comprehensive Root Canal Curvature Assessment Method (CRCAM): a novel seven-parameter system for predicting endodontic difficulty.BMC medical informatics and decision making · 2026Article
- Can super resolution via deep learning improve classification accuracy in dental radiography?Dento maxillo facial radiology · 2025Article
- RCFLA-YOLO: a deep learning-driven framework for the automated assessment of root canal filling quality in periapical radiographs.BMC medical education · 2025Article
- YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images.Sensors (Basel, Switzerland) · 2025Article
- Automatic detection of developmental stages of molar teeth with deep learning.BMC oral health · 2025Article
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Authors and funding
3 authors.
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Abstract
objectivesThis work proposes a novel method to evaluate root canal filling (RCF) success using artificial intelligence (AI) and image analysis techniques.
methods1121 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.
resultsModel 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.
conclusionsOur study demonstrates that AI-based solutions present accurate and reliable performance for root canal filling evaluation.
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