ArticleDiagnostics (Basel, Switzerland)2021
Automatic Detection of Mandibular Fractures in Panoramic Radiographs Using Deep Learning.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- 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
- 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
- Radiomics analysis of panoramic radiographs using machine learning for the detection of peri-implantitis.BMC oral health · 2026Article
- Deep learning-based fractured tooth detection in occlusal radiographs.BMC oral health · 2025Article
- Rehabilitation Driven Optimized YOLOv11 Model for Medical X-Ray Fracture Detection.Sensors (Basel, Switzerland) · 2025Article
- Detection of maxillary sinus pathologies using deep learning algorithms.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Deep learning-based assessment of pulp involvement in primary molars using YOLO v8.PLOS digital health · 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
- Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network.Journal of dental research · 2024Article
- Optimal Training Positive Sample Size Determination for Deep Learning with a Validation on CBCT Image Caries Recognition.Diagnostics (Basel, Switzerland) · 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
- Assessment of mandibular bony healing, mandibular condyle and angulus after orthognathic surgery using fractal dimension method.Medicina oral, patologia oral y cirugia bucal · 2024Article
- Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.Dento maxillo facial radiology · 2024Article
- Establishing a novel deep learning model for detecting peri-implantitiJournal of dental sciences · 2024Article
- Dental Caries Detection and Classification in CBCT Images Using Deep Learning.International dental journal · 2024Article
- "Air Sign" in Misdiagnosed Mandibular Fractures Based on CT and CBCT Evaluation.Diagnostics (Basel, Switzerland) · 2024Article
- Advanced Diagnostics of Respiratory Distress Syndrome in Premature Infants Treated with Surfactant and Budesonide through Computer-Assisted Chest X-ray Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Automating bone loss measurement on periapical radiographs for predicting the periodontitis stage and grade.Frontiers in dental medicine · 2024Article
- Assessing the reliability of CBCT-based AI-generated STL files in diagnosing osseous changes of the mandibular condyle: a comparative study with ground truth diagnosis.Dento maxillo facial radiology · 2023Article
- Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks.European journal of dentistry · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Mandibular fracture is one of the most frequent injuries in oral and maxillo-facial surgery. Radiologists diagnose mandibular fractures using panoramic radiography and cone-beam computed tomography (CBCT). Panoramic radiography is a conventional imaging modality, which is less complicated than CBCT. This paper proposes the diagnosis method of mandibular fractures in a panoramic radiograph based on a deep learning system without the intervention of radiologists. The deep learning system used has a one-stage detection called you only look once (YOLO). To improve detection accuracy, panoramic radiographs as input images are augmented using gamma modulation, multi-bounding boxes, single-scale luminance adaptation transform, and multi-scale luminance adaptation transform methods. Our results showed better detection performance than the conventional method using YOLO-based deep learning. Hence, it will be helpful for radiologists to double-check the diagnosis of mandibular fractures.
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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.