ArticleScientific reports2023
Automated segmentation of the mandibular canal and its anterior loop by deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Artificial intelligence-based segmentation of mandibular canal on cone-beam computed tomography: a systematic review and meta-analysis.Oral radiology · 2026Pooled it
- The role of artificial intelligence in preoperative planning for Total Hip Arthroplasty: a systematic review.Frontiers in artificial intelligence · 2024Pooled it
- Artificial Intelligence Reporting Guidelines in Dentomaxillofacial Radiology: Specialty-Oriented Practical Guide.Diagnostics (Basel, Switzerland) · 2026Review
- Fully automated deep learning for the identification and classification of dental implant brands from multimodal radiographic images.Clinical oral investigations · 2026Article
- Clinical Applicability of Artificial Intelligence-Driven Implant Planning and Surgical Guide Design in the Maxillary Esthetic Zone: A Registry-Based Cohort Study.Clinical oral implants research · 2026Article
- Investigation of the Use of Artificial Intelligence in Anterior Loop Detection: A Panoramic Radiography Study.Diagnostics (Basel, Switzerland) · 2026Article
- A BMC-Net model for the recognition and segmentation of mandibular canal bifurcation.Oral radiology · 2026Article
- Observation of the internal structure of the mandible related to implant placement in the mandibular anterior region.Anatomy & cell biology · 2026Article
- AI-Assisted Identification of the Medial Lingual Foramen on CBCT: A Deep Learning Approach for Preoperative Implant Assessment.Medicina (Kaunas, Lithuania) · 2026Article
- A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography.Scientific reports · 2026Article
- Deep learning-based automated positioning system for maxillary skeletal expander: development and clinical validation.Clinical oral investigations · 2026Article
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Digital Dental Radiology and Diagnostics-From 2D to 3D.Australian dental journal · 2025Review
- Deploying a novel deep learning framework for segmentation of specific anatomical structures on cone-beam CT.Oral radiology · 2025Article
- Comparison of AI-Powered Tools for CBCT-Based Mandibular Incisive Canal Segmentation: A Validation Study.Clinical oral implants research · 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
- AI-Driven Risk Stratification of the Lingual Foramen: A CBCT-Based Prevalence and Morphological Analysis.Healthcare (Basel, Switzerland) · 2025Article
- The Contribution of Real-Time Artificial Intelligence Segmentation in Maxillofacial Trauma Emergencies.Diagnostics (Basel, Switzerland) · 2025Article
- A novel artificial intelligence-powered tool for automated root canal segmentation in single-rooted teeth on cone-beam computed tomography.International endodontic journal · 2025Article
- A unique AI-based tool for automated segmentation of pulp cavity structures in maxillary premolars on CBCT.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate mandibular canal (MC) detection is crucial to avoid nerve injury during surgical procedures. Moreover, the anatomic complexity of the interforaminal region requires a precise delineation of anatomical variations such as the anterior loop (AL). Therefore, CBCT-based presurgical planning is recommended, even though anatomical variations and lack of MC cortication make canal delineation challenging. To overcome these limitations, artificial intelligence (AI) may aid presurgical MC delineation. In the present study, we aim to train and validate an AI-driven tool capable of performing accurate segmentation of the MC even in the presence of anatomical variation such as AL. Results achieved high accuracy metrics, with 0.997 of global accuracy for both MC with and without AL. The anterior and middle sections of the MC, where most surgical interventions are performed, presented the most accurate segmentation compared to the posterior section. The AI-driven tool provided accurate segmentation of the mandibular canal, even in the presence of anatomical variation such as an anterior loop. Thus, the presently validated dedicated AI tool may aid clinicians in automating the segmentation of neurovascular canals and their anatomical variations. It may significantly contribute to presurgical planning for dental implant placement, especially in the interforaminal region.
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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.