ArticleDento maxillo facial radiology2023
Detection of vertical root fractures by cone-beam computed tomography based on deep learning.
Article in Dento maxillo facial 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, 34 citations in OpenAlex.
- Review
- A phantom study on the evaluation of the YOLOv8 deep learning model in the detection of tooth ankylosis on cone-beam computed tomography images.Head & face medicine · 2026Article
- IGMNN: a diagnosis method for vertical root fractures based on an information gated memory neural network.Scientific reports · 2026Article
- Use of Artificial Intelligence in Diagnosing Vertical Root Fractures-A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Machine learning for classification of periodontal defects (vertical versus horizontal) using CBCT datasets.Bioinformation · 2026Article
- Deep learning-based fractured tooth detection in occlusal radiographs.BMC oral health · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs.BDJ open · 2024Article
- Application of deep learning and feature selection technique on external root resorption identification on CBCT images.BMC oral health · 2024Article
- Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset-A Validation Study.Journal of clinical medicine · 2023Article
- Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aims to evaluate the performance of ResNet models in the detection of
methodsA CBCT image dataset consisting of 28 teeth (14 intact and 14 teeth with VRF, 1641 slices) from 14 patients, and another dataset containing 60 teeth (30 intact and 30 teeth with VRF, 3665 slices) from an
resultsThe AUC of the models on the patient data were: 0.827(ResNet-18), 0.929(ResNet-50), and 0.882(ResNet-101). The AUC of the models on the mixed data get improved as:0.927(ResNet-18), 0.936(ResNet-50), and 0.893(ResNet-101). The maximum AUC were: 0.929 (0.908-0.950, 95% CI) and 0.936 (0.924-0.948, 95% CI) for the patient data and mixed data from ResNet-50, which is comparable to the AUC (0.937 and 0.950) for patient data and (0.915 and 0.935) for the mixed data obtained from the two oral and maxillofacial radiologists, respectively.
conclusionsDeep-learning models showed high accuracy in the detection of VRF using CBCT images. The data obtained from the in vitro VRF model increases the data scale, which is beneficial to the training of deep-learning models.
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