ArticleOdontology2024
Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images.
Article in Odontology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Use of artificial intelligence for detection of MB2 canals in maxillary first molars on CBCT: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- PanMB2-Net: a deep learning framework for preoperative MB2 canal risk assessment on panoramic radiographs.BMC oral health · 2026Article
- Deep learning-based detection of the second mesiobuccal canal in maxillary first molars using cone-beam computed tomography.BMC oral health · 2026Article
- Three-dimensional automatic segmentation of root canals with focus on the second mesiobuccal canal using nnU-Netv2 on CBCT images: deep learning approach.BMC oral health · 2026Article
- Deep learning detection of ectopic canines and molars in mixed dentition.Scientific reports · 2026Article
- Automatic recognition and measurement of anatomical structures associated with the elevation of the maxillary sinus floor by deep learning on cone-beam computed tomographic scans.BMC oral health · 2026Article
- Application of machine learning in predicting of MB2 canal in permanent maxillary first and second molars: A CBCT study.PloS one · 2026Article
- A comparison of methods for assessing root canal curvature using periapical radiographs: an intrarater/interrater reliability and agreement study.BMC oral health · 2025Article
- Two step approach for detecting and segmenting the second mesiobuccal canal of maxillary first molars on cone beam computed tomography (CBCT) images via artificial intelligence.BMC oral health · 2025Observational
- Artificial Intelligence in the Study of Root and Canal Anatomy: A Comprehensive Review on Applications, Advantages, Challenges and Future Directions.European endodontic journal · 2025Review
- Segmentation of the nasopalatine canal and detection of canal furcation status with artificial intelligence on cone-beam computed tomography images.Oral radiology · 2025Article
- Artificial Intelligence Classification for Detecting and Grading Lumbar Intervertebral Disc Degeneration.Spine surgery and related research · 2024Article
- Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning.BMC oral health · 2024Article
- Precision Medicine for Apical Lesions and Peri-Endo Combined Lesions Based on Transfer Learning Using Periapical Radiographs.Bioengineering (Basel, Switzerland) · 2024Article
- The Use of Artificial Intelligence in Endodontics.Journal of dental research · 2024Review
- Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.Dento maxillo facial radiology · 2024Article
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 3 countries.
Funding
Abstract
The objective of this study is to use a deep-learning model based on CNN architecture to detect the second mesiobuccal (MB2) canals, which are seen as a variation in maxillary molars root canals. In the current study, 922 axial sections from 153 patients' cone beam computed tomography (CBCT) images were used. The segmentation method was employed to identify the MB2 canals in maxillary molars that had not previously had endodontic treatment. Labeled images were divided into training (80%), validation (10%) and testing (10%) groups. The artificial intelligence (AI) model was trained using the You Only Look Once v5 (YOLOv5x) architecture with 500 epochs and a learning rate of 0.01. Confusion matrix and receiver-operating characteristic (ROC) analysis were used in the statistical evaluation of the results. The sensitivity of the MB2 canal segmentation model was 0.92, the precision was 0.83, and the F1 score value was 0.87. The area under the curve (AUC) in the ROC graph of the model was 0.84. The mAP value at 0.5 inter-over union (IoU) was found as 0.88. The deep-learning algorithm used showed a high success in the detection of the MB2 canal. The success of the endodontic treatment can be increased and clinicians' time can be preserved using the newly created artificial intelligence-based models to identify variations in root canal anatomy before the treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.