ArticleOral radiology2023
Experimental validation of computer-vision methods for the successful detection of endodontic treatment obturation and progression from noisy radiographs.
Article in Oral radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- Developing an AI-powered tool for radiographic feedback on working length determination in pre-clinical endodontic training.Frontiers in dental medicine · 2026Article
- Error-Tolerant Multimodal Vision-Language Models for Endodontic Triaging: A Cross-Sectional Study.International journal of dentistry · 2026Article
- Computational Insights into Root Canal Treatment: A Survey of Selected Methods in Imaging, Segmentation, Morphological Analysis, and Clinical Management.Dentistry journal · 2025Review
- RCFLA-YOLO: a deep learning-driven framework for the automated assessment of root canal filling quality in periapical radiographs.BMC medical education · 2025Article
- Prevalence and root canal morphology of taurodontism analyzed by cone-beam computed tomography in Northern China.BMC oral health · 2025Article
- Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.Clinical and experimental dental research · 2024Review
- Artificial Intelligence in Endodontics: A Scoping Review.Iranian endodontic journal · 2024Article
- Clinical Annotation and Segmentation Tool (CAST) Implementation for Dental Diagnostics.Cureus · 2023Article
- Article
- Influence of Intraoral Scanners, Operators, and Data Processing on Dimensional Accuracy of Dental Casts for Unsupervised Clinical Machine Learning: An In Vitro Comparative Study.International journal of dentistry · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose(1) To evaluate the effects of denoising and data balancing on deep learning to detect endodontic treatment outcomes from radiographs. (2) To develop and train a deep-learning model and classifier to predict obturation quality from radiomics.
methodsThe study conformed to the STARD 2015 and MI-CLAIMS 2021 guidelines. 250 deidentified dental radiographs were collected and augmented to produce 2226 images. The dataset was classified according to endodontic treatment outcomes following a set of customized criteria. The dataset was denoised and balanced, and processed with YOLOv5s, YOLOv5x, and YOLOv7 models of real-time deep-learning computer vision. Diagnostic test parameters such as sensitivity (Sn), specificity (Sp), accuracy (Ac), precision, recall, mean average precision (mAP), and confidence were evaluated.
resultsOverall accuracy for all the deep-learning models was above 85%. Imbalanced datasets with noise removal led to YOLOv5x's prediction accuracy to drop to 72%, while balancing and noise removal led to all three models performing at over 95% accuracy. mAP saw an improvement from 52 to 92% following balancing and denoising.
conclusionThe current study of computer vision applied to radiomic datasets successfully classified endodontic treatment obturation and mishaps according to a custom progressive classification system and serves as a foundation to larger research on the subject matter.
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