Observational studyBMC oral health2025
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.
Observational study in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Accuracy of Computer Aided Detection of Second Mesio-buccal Canal of Maxillary First Molars on CBCT Images Using Deep Learning Model (Artificial Intelligence): Diagnostic Accuracy Study
Diagnostic Accuracy of a Deep Learning Model (Artificial Intelligence) for Detecting Extra Root Canals in Mandibular Premolars on CBCT Images: Diagnostic Accuracy Study.
Who cites it
5 citing papers in PubMed.
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- A multicenter-validated interpretable transformer model for pituitary microadenoma detection on non-contrast multiparametric MRI.BMC medical imaging · 2026Article
- Deep learning-based detection of the second mesiobuccal canal in maxillary first molars using cone-beam computed tomography.BMC oral health · 2026Article
- Deep learning-based automatic segmentation of MRONJ lesions on CBCT images.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
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThe purpose of this study was to assess the accuracy of a customized deep learning model based on CNN and U-Net for detecting and segmenting the second mesiobuccal canal (MB2) of maxillary first molar teeth on cone beam computed tomography (CBCT) scans. METHODOLOGY: CBCT scans of 37 patients were imported into 3D slicer software to crop and segment the canals of the mesiobuccal (MB) root of the maxillary first molar. The annotated data were divided into two groups: 80% for training and validation and 20% for testing. The data were used to train the AI model in 2 separate steps: a classification model based on a customized CNN and a segmentation model based on U-Net. A confusion matrix and receiver-operating characteristic (ROC) analysis were used in the statistical evaluation of the results of the classification model, whereas the Dice-coefficient (DCE) was used to express the segmentation accuracy.
resultsF1 score, testing accuracy, recall and precision values were 0.93, 0.87, 1.0 and 0.87 respectively, for the cropped images of MB root of maxillary 1st molar teeth in the testing group. The testing loss was 0.4, and the area under the curve (AUC) value was 0.57. The segmentation accuracy results were satisfactory, where the DCE of training was 0.85 and DCE of testing was 0.79.
conclusionMB2 in the maxillary first molar can be precisely detected and segmented via the developed AI algorithm in CBCT images.
trial registrationCurrent Controlled Trial Number NCT05340140. April 22, 2022.
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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.