ArticleBMC oral health2022
Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images.
Article in BMC oral health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Diagnostic efficacy of cone-beam computed tomography for detection of vertical root fractures in endodontically treated teeth: a systematic review.BMC medical imaging · 2023Pooled it
- Investigation of the Use of Artificial Intelligence in Anterior Loop Detection: A Panoramic Radiography Study.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.BMC oral health · 2026Article
- 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
- Optical Coherence Tomography for Detection of Dental Cracks and Vertical Root Fracture: A Scoping Review.Clinical and experimental dental research · 2026Article
- Fracture detection of distal radius using deep-learning-based dual-channel feature fusion algorithm.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2026Article
- SBMN: Similarity-Based Memory Network for the Diagnosis of Vertical Root Fracture in Dental Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- IGMNN: a diagnosis method for vertical root fractures based on an information gated memory neural network.Scientific reports · 2026Article
- AI in endodontics: enhancing precision, efficiency, and personalized care.Annals of medicine and surgery (2012) · 2026Article
- Use of Artificial Intelligence in Diagnosing Vertical Root Fractures-A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Revolutionizing endodontics: the impact and innovations of artificial intelligence.BMC oral health · 2026Review
- Five-year follow-up of conservative management for root fractures in immature permanent tooth post trauma: A case report and literature review.SAGE open medical case reports · 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
- Embracing technological revolution: A panorama of machine learning in dentistry.Medicina oral, patologia oral y cirugia bucal · 2024Review
- Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs.BDJ open · 2024Article
- Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making.Diagnostics (Basel, Switzerland) · 2024Review
- Deep learning for determining the difficulty of endodontic treatment: a pilot study.BMC oral health · 2024Article
- Application of deep learning in isolated tooth identification.BMC oral health · 2024Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesEvaluating the diagnostic efficiency of deep learning models to diagnose vertical root fracture in vivo on cone-beam CT (CBCT) images. MATERIALS AND
methodsThe CBCT images of 276 teeth (138 VRF teeth and 138 non-VRF teeth) were enrolled and analyzed retrospectively. The diagnostic results of these teeth were confirmed by two chief radiologists. There were two experimental groups: auto-selection group and manual selection group. A total of 552 regions of interest of teeth were cropped in manual selection group and 1118 regions of interest of teeth were cropped in auto-selection group. Three deep learning networks (ResNet50, VGG19 and DenseNet169) were used for diagnosis (3:1 for training and testing). The diagnostic efficiencies (accuracy, sensitivity, specificity, and area under the curve (AUC)) of three networks were calculated in two experiment groups. Meanwhile, 552 teeth images in manual selection group were diagnosed by a radiologist. The diagnostic efficiencies of the three deep learning network models in two experiment groups and the radiologist were calculated.
resultsIn manual selection group, ResNet50 presented highest accuracy and sensitivity for diagnosing VRF teeth. The accuracy, sensitivity, specificity and AUC was 97.8%, 97.0%, 98.5%, and 0.99, the radiologist presented accuracy, sensitivity, and specificity as 95.3%, 96.4 and 94.2%. In auto-selection group, ResNet50 presented highest accuracy and sensitivity for diagnosing VRF teeth, the accuracy, sensitivity, specificity and AUC was 91.4%, 92.1%, 90.7% and 0.96.
conclusionIn manual selection group, ResNet50 presented higher diagnostic efficiency in diagnosis of in vivo VRF teeth than VGG19, DensenNet169 and radiologist with 2 years of experience. In auto-selection group, Resnet50 also presented higher diagnostic efficiency in diagnosis of in vivo VRF teeth than VGG19 and DensenNet169. This makes it a promising auxiliary diagnostic technique to screen for VRF teeth.
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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.