ArticleDento maxillo facial radiology2021
Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs.
Article in Dento maxillo facial radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Review
- Artificial Intelligence in Endodontics-A Bibliometric Analysis.Australian endodontic journal : the journal of the Australian Society of Endodontology Inc · 2026Review
- Automated detection of C-shaped canals in mandibular second molars from panoramic radiographs: comparing single and ensemble convolutional neural networks within a 2-stage pipeline.Clinical oral investigations · 2026Article
- Assessing the diagnostic performance of MediXpar texture analysis for C-shaped canal morphology on panoramic radiographs: a pilot study.Clinical oral investigations · 2026Article
- AI in endodontics: enhancing precision, efficiency, and personalized care.Annals of medicine and surgery (2012) · 2026Article
- Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review.European endodontic journal · 2025Review
- A Logistic Regression Model for Predicting Osteoporosis Using Alveolar Bone Mineral Density Measured on Intraoral Radiographs Combined with Panoramic Mandibular Cortical Index.Journal of clinical medicine · 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
- Preparing for downstream tasks in artificial intelligence for dental radiology: a baseline performance comparison of deep learning models.Dento maxillo facial radiology · 2025Article
- Establishment and evaluation of a deep learning-based tooth wear severity grading system using intraoral photographs.Journal of dental sciences · 2025Article
- Detection of three-rooted mandibular first molars on panoramic radiographs using deep learning.Scientific reports · 2024Article
- Artificial intelligence application in endodontics: A narrative review.Imaging science in dentistry · 2024Review
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images.Odontology · 2024Article
- Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs.Imaging science in dentistry · 2024Article
- Artificial Intelligence in Endodontics: A Scoping Review.Iranian endodontic journal · 2024Article
- Association of root morphology of mandibular second molars on panoramic-like and axial views of cone-beam computed tomography.BMC oral health · 2023Article
- Detection of unilateral and bilateral cleft alveolus on panoramic radiographs using a deep-learning system.Dento maxillo facial radiology · 2023Article
- Evaluating the performance of generative adversarial network-synthesized periapical images in classifying C-shaped root canals.Scientific reports · 2023Article
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7 authors.
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Abstract
objectiveThe aim of this study was to evaluate the use of a convolutional neural network (CNN) system for predicting C-shaped canals in mandibular second molars on panoramic radiographs.
methodsPanoramic and cone beam CT (CBCT) images obtained from June 2018 to May 2020 were screened and 1020 patients were selected. Our dataset of 2040 sound mandibular second molars comprised 887 C-shaped canals and 1153 non-C-shaped canals. To confirm the presence of a C-shaped canal, CBCT images were analyzed by a radiologist and set as the gold standard. A CNN-based deep-learning model for predicting C-shaped canals was built using Xception. The training and test sets were set to 80 to 20%, respectively. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, and precision. Receiver-operating characteristics (ROC) curves were drawn, and the area under the curve (AUC) values were calculated. Further, gradient-weighted class activation maps (Grad-CAM) were generated to localize the anatomy that contributed to the predictions.
resultsThe accuracy, sensitivity, specificity, and precision of the CNN model were 95.1, 92.7, 97.0, and 95.9%, respectively. Grad-CAM analysis showed that the CNN model mainly identified root canal shapes converging into the apex to predict the C-shaped canals, while the root furcation was predominantly used for predicting the non-C-shaped canals.
conclusionsThe deep-learning system had significant accuracy in predicting C-shaped canals of mandibular second molars on panoramic radiographs.
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