ArticleDento maxillo facial radiology2021
Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle.
Article in Dento maxillo facial radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- 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
- Deep learning-based fractured tooth detection in occlusal radiographs.BMC oral health · 2025Article
- Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review.European endodontic journal · 2025Review
- Automatic detection of orthodontically induced external root resorption based on deep convolutional neural networks using CBCT images.Scientific reports · 2025Article
- Improving TMJ Diagnosis: A Deep Learning Approach for Detecting Mandibular Condyle Bone Changes.Diagnostics (Basel, Switzerland) · 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
- Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images.Oral radiology · 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
- Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs.Imaging science in dentistry · 2024Article
- "Air Sign" in Misdiagnosed Mandibular Fractures Based on CT and CBCT Evaluation.Diagnostics (Basel, Switzerland) · 2024Article
- An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population.BMC oral health · 2023Article
- Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts.Scientific reports · 2023Article
- Artificial intelligence in detecting dentomaxillofacial fractures in diagnostic imaging: a scoping review protocol.BMJ open · 2023Article
- Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Effect of deep transfer learning with a different kind of lesion on classification performance of pre-trained model: Verification with radiolucent lesions on panoramic radiographs.Imaging science in dentistry · 2023Article
- Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging.Dento maxillo facial radiology · 2023Review
- Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe present study aimed to verify the classification performance of deep learning (DL) models for diagnosing fractures of the mandibular condyle on panoramic radiographs using data sets from two hospitals and to compare their internal and external validities.
methodsPanoramic radiographs of 100 condyles with and without fractures were collected from two hospitals and a fivefold cross-validation method was employed to construct and evaluate the DL models. The internal and external validities of classification performance were evaluated as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).
resultsFor internal validity, high classification performance was obtained, with AUC values of >0.85. Conversely, external validity for the data sets from the two hospitals exhibited low performance. Using combined data sets from both hospitals, the DL model exhibited high performance, which was slightly superior or equal to that of the internal validity but without a statistically significant difference.
conclusionThe constructed DL model can be clinically employed for diagnosing fractures of the mandibular condyle using panoramic radiographs. However, the domain shift phenomenon should be considered when generalizing DL systems.
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