Research graph
References from REMOVED: Human-AI collaboration for mandibular canal tracing accuracy on cone-beam computed tomography: A multi-evaluator study. Local targets link to admitted publications; unresolved targets remain external evidence.
Comparison between digital panoramic radiography and cone-beam computed tomography for the identification of the mandibular canal as part of presurgical dental implant assessment
10.1016/j.joms.2008.06.021 · 2008 · External reference
Accuracy of software designed for automated localization of the inferior alveolar nerve canal on cone beam CT images
2015 · External reference
Evaluation of the diagnostic efficacy of two cone beam computed tomography protocols in reliably detecting the location of the inferior alveolar nerve canal
10.1259/dmfr.20160389 · 2017 · External reference
Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes
10.1038/s41598-020-62321-3 · 2020 · External reference
Accuracy of artificial intelligence-based segmentation of the mandibular canal in CBCT
10.1111/clr.14307 · 2024 · External reference
Validation of different protocols of inferior alveolar canal tracing using cone beam computed tomography (CBCT)
10.1259/dmfr.20220016 · 2022 · External reference
Automatic mandibular canal detection using a deep convolutional neural network
10.1038/s41598-020-62586-8 · 2020 · External reference
Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset
10.1038/s41598-023-40516-8 · 2023 · External reference
Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT
10.1016/j.jdent.2021.103891 · 2022 · External reference
The effectiveness of semi-automated and fully automatic segmentation for inferior alveolar canal localization on CBCT scans: a systematic review
10.3390/ijerph19010560 · 2022 · External reference
CBCT segmentation of the mandibular canal with both semi-automated and fully automated methods: a systematic review
2024 · External reference
Augmented intelligence in oral and maxillofacial radiology: a systematic review
10.1016/j.oooo.2025.03.015 · 2025 · External reference
Efficacy of a deep learning system for automatic analysis of the comprehensive spatial relationship between the mandibular third molar and inferior alveolar canal on panoramic radiographs
10.1016/j.oooo.2024.12.020 · 2025 · External reference
Automatic segmentation of mandibular canal using transformer based neural networks
10.3389/fbioe.2023.1302524 · 2023 · External reference
Automated segmentation of the mandibular canal and its anterior loop by deep learning
10.1038/s41598-023-37798-3 · 2023 · External reference
Statistical validation of image segmentation quality based on a spatial overlap index
10.1016/s1076-6332(03)00671-8 · 2004 · External reference
Accurate mandibular canal segmentation of dental CBCT using a two-stage 3D-UNet based segmentation framework
10.1186/s12903-023-03279-2 · 2023 · External reference
Robust and accurate mandible segmentation on dental CBCT scans affected by metal artifacts using a prior shape model
10.3390/jpm11050364 · 2021 · External reference
Automatic segmentation of mandibular canal in cone beam CT images using conditional statistical shape model and fast marching
10.1007/s11548-016-1484-2 · 2017 · External reference
Artificial intelligence model to detect real contact relationship between mandibular third molars and inferior alveolar nerve based on panoramic radiographs
10.3390/diagnostics11091664 · 2021 · External reference
Diagnostic agreement of conventional and inverted scanned panoramic radiographs in the detection of the mandibular canal and the mental foramen
10.1563/1548-1336(2004)030<0002:daocai>2.0.co;2 · 2004 · External reference
Inferior alveolar canal automatic detection with deep learning CNNs on CBCTs: development of a novel model and release of open-source dataset and algorithm
10.3390/app13053271 · 2023 · External reference
Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques
10.1016/j.oooo.2022.05.014 · 2022 · External reference
AI-powered segmentation of bifid mandibular canals using CBCT
10.1186/s12903-025-06311-9 · 2025 · External reference
Measuring anterior loop length of the inferior alveolar nerve to estimate safe zone in implant planning: a CBCT study in a Malaysian population
10.1016/j.prosdent.2017.10.019 · 2018 · External reference
Interexaminer agreement between two dental specialties for the detection of bifid mandibular canal and accessory mental foramen in cone-beam computed tomography
10.17126/joralres.2022.010 · 2022 · External reference
A unique artificial intelligence-based tool for automated CBCT segmentation of mandibular incisive canal
10.1259/dmfr.20230321 · 2023 · External reference
Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans
10.1016/j.jormas.2020.12.006 · 2021 · External reference
A deep learning approach for dental implant planning in cone-beam computed tomography images
10.1186/s12880-021-00618-z · 2021 · External reference
Unresolved reference
2025 · External reference
Unresolved reference
2022 · External reference
Artificial intelligence (A.I.) in dental curricula: Ethics and responsible integration
10.1002/jdd.13337 · 2023 · External reference
Promises and perils of artificial intelligence in dentistry
10.1111/adj.12812 · 2021 · External reference
Unresolved reference
2023 · External reference
