SynthesisInternational journal of environmental research and public health2022
The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review.
Synthesis in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparisons of artificial intelligence automated segmentation techniques to manual segmentation techniques of the maxilla and maxillary sinus for CT or cone-beam CT scans-a systematic review.Dento maxillo facial radiology · 2025Pooled it
- Fully automated deep learning for the identification and classification of dental implant brands from multimodal radiographic images.Clinical oral investigations · 2026Article
- AI-assisted preoperative surgical planning for dental implant.Journal of translational medicine · 2025Article
- Assessing the spatial relationship between mandibular third molars and the inferior alveolar canal using a deep learning-based approach: a proof-of-concept study.BMC oral health · 2025Article
- Convolutional neural network for maxillary sinus segmentation based on the U-Net architecture at different planes in the Chinese population: a semantic segmentation study.BMC oral health · 2025Article
- Assessing the accuracy of artificial intelligence in mandibular canal segmentation compared to semi-automatic segmentation on cone-beam computed tomography images.Polish journal of radiology · 2025Article
- Artificial Intelligence-Assisted Segmentation of a Falx Cerebri Calcification on Cone-Beam Computed Tomography: A Case Report.Medicina (Kaunas, Lithuania) · 2024Article
- Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network.Journal of dental research · 2024Article
- Reliability of the AI-Assisted Assessment of the Proximity of the Root Apices to Mandibular Canal.Journal of clinical medicine · 2024Article
- Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions.Bioengineering (Basel, Switzerland) · 2024Article
- Accuracy of facial skeletal surfaces segmented from CT and CBCT radiographs.Scientific reports · 2023Article
- The Influence of Slice Thickness, Sharpness, and Contrast Adjustments on Inferior Alveolar Canal Segmentation on Cone-Beam Computed Tomography Scans: A Retrospective Study.Journal of personalized medicine · 2023Article
- Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset.Scientific reports · 2023Article
- Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls.Diagnostics (Basel, Switzerland) · 2023Article
- Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review.Medicina (Kaunas, Lithuania) · 2023Review
- Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies.Diagnostics (Basel, Switzerland) · 2023Article
- Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
This systematic review aims to identify the available semi-automatic and fully automatic algorithms for inferior alveolar canal localization as well as to present their diagnostic accuracy. Articles related to inferior alveolar nerve/canal localization using methods based on artificial intelligence (semi-automated and fully automated) were collected electronically from five different databases (PubMed, Medline, Web of Science, Cochrane, and Scopus). Two independent reviewers screened the titles and abstracts of the collected data, stored in EndnoteX7, against the inclusion criteria. Afterward, the included articles have been critically appraised to assess the quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Seven studies were included following the deduplication and screening against exclusion criteria of the 990 initially collected articles. In total, 1288 human cone-beam computed tomography (CBCT) scans were investigated for inferior alveolar canal localization using different algorithms and compared to the results obtained from manual tracing executed by experts in the field. The reported values for diagnostic accuracy of the used algorithms were extracted. A wide range of testing measures was implemented in the analyzed studies, while some of the expected indexes were still missing in the results. Future studies should consider the new artificial intelligence guidelines to ensure proper methodology, reporting, results, and validation.
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