ArticleScientific reports2024
Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic, predictive, and therapeutic approaches for impacted canines: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- YOLO11-guided swin transformer for molar occlusion classification.Scientific reports · 2026Article
- Reliability of a Deep Learning Model in Predicting Permanent Maxillary Canine Eruption for Preventive Orthodontics: A Comparative Study with Orthodontists.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in the Radiological Diagnosis of Impacted Maxillary Canines: A Systematic Review.Journal of clinical medicine · 2026Review
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Artificial intelligence performance in maxillary canine impaction: a systematic review.European journal of medical research · 2026Review
- Deep learning-based 3D automatic segmentation of impacted canines in CBCT scans.BMC oral health · 2025Article
- Article
- Current progress of digital twin construction using medical imaging.Journal of applied clinical medical physics · 2025Review
- Evaluation of Caries Detection on Bitewing Radiographs: A Comparative Analysis of the Improved Deep Learning Model and Dentist Performance.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2025Article
- Automated Cone Beam Computed Tomography Segmentation of Multiple Impacted Teeth With or Without Association to Rare Diseases: Evaluation of Four Deep Learning-Based Methods.Orthodontics & craniofacial research · 2025Article
- The Contribution of Real-Time Artificial Intelligence Segmentation in Maxillofacial Trauma Emergencies.Diagnostics (Basel, Switzerland) · 2025Article
- A unique AI-based tool for automated segmentation of pulp cavity structures in maxillary premolars on CBCT.Scientific reports · 2025Article
- Can digital twin technology revolutionize public health emergency management? Insights from the PPRR framework.Frontiers in public health · 2025Article
- Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.International journal of paediatric dentistry · 2025Article
- A hierarchical deep learning approach for diagnosing impacted canine-induced root resorption via cone-beam computed tomography.BMC oral health · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The process of creating virtual models of dentomaxillofacial structures through three-dimensional segmentation is a crucial component of most digital dental workflows. This process is typically performed using manual or semi-automated approaches, which can be time-consuming and subject to observer bias. The aim of this study was to train and assess the performance of a convolutional neural network (CNN)-based online cloud platform for automated segmentation of maxillary impacted canine on CBCT image. A total of 100 CBCT images with maxillary canine impactions were randomly allocated into two groups: a training set (n = 50) and a testing set (n = 50). The training set was used to train the CNN model and the testing set was employed to evaluate the model performance. Both tasks were performed on an online cloud-based platform, 'Virtual patient creator' (Relu, Leuven, Belgium). The performance was assessed using voxel- and surface-based comparison between automated and semi-automated ground truth segmentations. In addition, the time required for segmentation was also calculated. The automated tool showed high performance for segmenting impacted canines with a dice similarity coefficient of 0.99 ± 0.02. Moreover, it was 24 times faster than semi-automated approach. The proposed CNN model achieved fast, consistent, and precise segmentation of maxillary impacted canines.
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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.