ArticleClinical oral investigations2022
Detection and classification of mandibular fracture on CT scan using deep convolutional neural network.
Article in Clinical oral investigations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 4 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence for fracture detection on computed tomography: a comprehensive systematic review and meta-analysis of diagnostic test accuracy in non-commercial and commercial solutions.Emergency radiology · 2026Pooled it
- Diagnostic performance of artificial intelligence for facial fracture detection: a systematic review.Oral radiology · 2026Pooled it
- Evaluation of Artificial Intelligence-based diagnosis for facial fractures, advantages compared with conventional imaging diagnosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis.Emergency radiology · 2025Pooled it
- Artificial Intelligence in Plastic and Reconstructive Surgery: A Bibliometric Study of the 50 Most-cited Articles.Plastic and reconstructive surgery. Global open · 2026Article
- Evaluation of an AI-based segmentation model for mandibular fracture segmentation on CBCT using volumetric and 2D slice-based validation approaches.BMC oral health · 2026Article
- An Interpretable Hybrid SFNet Deep Learning Framework for Multi-Site Bone Fracture Detection in Medical Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Artificial Intelligence in Bone Fracture Detection: A Review of Evidence, Limitations, and Clinical Integration.Cureus · 2025Review
- [Artificial intelligence in stomatology: Innovations in clinical practice, research, education, and healthcare management].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025Review
- Artificial Intelligence and Machine Learning in Reconstructive Microsurgery.Seminars in plastic surgery · 2025Review
- Toward Precision Diagnosis of Maxillofacial Pathologies by Artificial Intelligence Algorithms: A Systematic Review.Journal of maxillofacial and oral surgery · 2025Review
- Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis.International dental journal · 2025Article
- MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging.Journal of imaging · 2024Article
- Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network.Journal of dental research · 2024Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Bibliometric Analysis of the Role of Artificial Intelligence in Detecting Maxillofacial Fractures.Cureus · 2024Review
- A dual-labeled dataset and fusion model for automatic teeth segmentation, numbering, and state assessment on panoramic radiographs.BMC oral health · 2024Article
- Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.Dento maxillo facial radiology · 2024Article
- Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
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Authors and funding
9 authors.
Funding
Abstract
objectivesThis study aimed to evaluate the accuracy and reliability of convolutional neural networks (CNNs) for the detection and classification of mandibular fracture on spiral computed tomography (CT). MATERIALS AND
methodsBetween January 2013 and July 2020, 686 patients with mandibular fractures who underwent CT scan were classified and annotated by three experienced maxillofacial surgeons serving as the ground truth. An algorithm including two convolutional neural networks (U-Net and ResNet) was trained, validated, and tested using 222, 56, and 408 CT scans, respectively. The diagnostic performance of the algorithm was compared with the ground truth and evaluated by DICE, accuracy, sensitivity, specificity, and area under the ROC curve (AUC).
resultsOne thousand five hundred six mandibular fractures in nine subregions of 686 patients were diagnosed. The DICE of mandible segmentation using U-Net was 0.943. The accuracies of nine subregions were all above 90%, with a mean AUC of 0.956.
conclusionsCNNs showed comparable reliability and accuracy in detecting and classifying mandibular fractures on CT. CLINICAL RELEVANCE: The algorithm for automatic detection and classification of mandibular fractures will help improve diagnostic efficiency and provide expertise to areas with lower medical levels.
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