ArticleBMC oral health2021
Deep semi-supervised learning for automatic segmentation of inferior alveolar nerve using a convolutional neural network.
Article in BMC oral health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 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
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Fully automated deep learning for the identification and classification of dental implant brands from multimodal radiographic images.Clinical oral investigations · 2026Article
- Deep learning for automatic segmentation of the inferior alveolar nerve using a hybrid CNN-transformer framework.Scientific reports · 2026Article
- PMCanalSeg: A dataset for automatic segmentation of the pterygopalatine and mandibular canals from 3D CBCT images.Scientific data · 2026Article
- AI-assisted preoperative surgical planning for dental implant.Journal of translational medicine · 2025Article
- Deep learning for automated mandibular canal segmentation in CBCT scans.BMC oral health · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Automated assessment and detection of third molar and inferior alveolar nerve relations using UNet and transfer learning models.Scientific reports · 2025Article
- Development and verification of a convolutional neural network-based model for automatic mandibular canal localization on multicenter CBCT images.BMC oral health · 2025Article
- Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
- Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography.Scientific reports · 2025Article
- Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study.Dento maxillo facial radiology · 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
- A two-stage deep-learning model for determination of the contact of mandibular third molars with the mandibular canal on panoramic radiographs.BMC oral health · 2024Article
- [Strategies for prevention and treatment of vascular and nerve injuries in mandibular anterior implant surgery].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2024Review
- Post-Traumatic Trigeminal Neuropathic Pain: A Narrative Review of Understanding, Management, and Prognosis.Biomedicines · 2024Review
- Performance of artificial intelligence using cone-beam computed tomography for segmentation of oral and maxillofacial structures: A systematic review and meta-analysis.Journal of clinical and experimental dentistry · 2023Review
- The Application of Deep Learning on CBCT in Dentistry.Diagnostics (Basel, Switzerland) · 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
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe inferior alveolar nerve (IAN) innervates and regulates the sensation of the mandibular teeth and lower lip. The position of the IAN should be monitored prior to surgery. Therefore, a study using artificial intelligence (AI) was planned to image and track the position of the IAN automatically for a quicker and safer surgery.
methodsA total of 138 cone-beam computed tomography datasets (Internal: 98, External: 40) collected from multiple centers (three hospitals) were used in the study. A customized 3D nnU-Net was used for image segmentation. Active learning, which consists of three steps, was carried out in iterations for 83 datasets with cumulative additions after each step. Subsequently, the accuracy of the model for IAN segmentation was evaluated using the 50 datasets. The accuracy by deriving the dice similarity coefficient (DSC) value and the segmentation time for each learning step were compared. In addition, visual scoring was considered to comparatively evaluate the manual and automatic segmentation.
resultsAfter learning, the DSC gradually increased to 0.48 ± 0.11 to 0.50 ± 0.11, and 0.58 ± 0.08. The DSC for the external dataset was 0.49 ± 0.12. The times required for segmentation were 124.8, 143.4, and 86.4 s, showing a large decrease at the final stage. In visual scoring, the accuracy of manual segmentation was found to be higher than that of automatic segmentation.
conclusionsThe deep active learning framework can serve as a fast, accurate, and robust clinical tool for demarcating IAN location.
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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.