ArticleScientific reports2022
Detection of mandibular fractures on panoramic radiographs using deep learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic performance of artificial intelligence for facial fracture detection: a systematic review.Oral radiology · 2026Pooled it
- Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.BMC oral health · 2025Pooled 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
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Artificial Intelligence in Oral and Maxillofacial Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
- 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
- AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.BMC oral health · 2026Article
- Deep learning-based fractured tooth detection in occlusal radiographs.BMC oral health · 2025Article
- Mandibular condyle detection using deep learning and double attractor-based energy valley optimizer algorithm.BMC oral health · 2025Article
- The Contribution of Real-Time Artificial Intelligence Segmentation in Maxillofacial Trauma Emergencies.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Faster R-CNN model for target recognition and diagnosis of scapular fractures.Journal of bone oncology · 2025Article
- Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis.International dental journal · 2025Article
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 2025Review
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network.Journal of dental research · 2024Article
- AI-Powered Identification of Osteoporosis in Dental Panoramic Radiographs: Addressing Methodological Flaws in Current Research.Diagnostics (Basel, Switzerland) · 2024Article
- Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.Dento maxillo facial radiology · 2024Article
- Evaluation of deep learning and convolutional neural network algorithms for mandibular fracture detection using radiographic images: A systematic review and meta-analysis.Imaging science in dentistry · 2024Review
- "Air Sign" in Misdiagnosed Mandibular Fractures Based on CT and CBCT Evaluation.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Mandibular fractures are among the most frequent facial traumas in oral and maxillofacial surgery, accounting for 57% of cases. An accurate diagnosis and appropriate treatment plan are vital in achieving optimal re-establishment of occlusion, function and facial aesthetics. This study aims to detect mandibular fractures on panoramic radiographs (PR) automatically. 1624 PR with fractures were manually annotated and labelled as a reference. A deep learning approach based on Faster R-CNN and Swin-Transformer was trained and validated on 1640 PR with and without fractures. Subsequently, the trained algorithm was applied to a test set consisting of 149 PR with and 171 PR without fractures. The detection accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an F1 score of 0.947 and an AUC of 0.977. Deep learning-based assistance of clinicians may reduce the misdiagnosis and hence the severe complications.
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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.