ArticleImaging science in dentistry2022
Evaluation of maxillary sinusitis from panoramic radiographs and cone-beam computed tomographic images using a convolutional neural network.
Article in Imaging science in dentistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.International dental journal · 2024Pooled it
- Deep learning in the diagnosis of maxillary sinus diseases: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Performance of panoramic radiography compared with computed tomography in the evaluation of pathological changes in the maxillary sinuses: a systematic review and meta-analysis.Dento maxillo facial radiology · 2023Pooled it
- Automatic Detection of Sinus Membrane Thickness Using a Hybrid Deep Learning Model Integrating Multiple Attention Mechanisms.Journal of clinical medicine · 2026Article
- Impact of Imaging Modality on AI-Based Detection of Incidental Maxillary Sinus Pathology: Comparison of Panoramic Radiography and CBCT.Diagnostics (Basel, Switzerland) · 2026Article
- Automated detection and classification of maxillary sinus variations using slice-based and full-volume CBCT deep learning models.BMC oral health · 2026Article
- Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset for Benchmarking Deep Learning Algorithms.Scientific data · 2026Article
- Evaluation of Maxillary Sinus Membrane Morphology Using a Novel Hybrid CNN-ViT-Based Deep Learning Model: An Automated Classification Study.Diagnostics (Basel, Switzerland) · 2026Article
- How artificial intelligence is reshaping citation impact in oral and maxillofacial radiology journals: an 8-year analysis with editorial and clinical implications (JCR 2017-2024).Frontiers in research metrics and analytics · 2026Article
- Review
- Diagnostic accuracy of artificial intelligence in the detection of maxillary sinus pathology using computed tomography: A concise systematic review.Imaging science in dentistry · 2025Article
- Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs.Bioengineering (Basel, Switzerland) · 2025Article
- Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model.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 · 2024Article
- Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning.BMC oral health · 2024Article
- Evaluation of the Alveolar Crest and Cemento-Enamel Junction in Periodontitis Using Object Detection on Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2024Article
- Development and Validation of an Ultrasonography-Based Machine Learning Model for Predicting Outcomes of Bruxism Treatments.Diagnostics (Basel, Switzerland) · 2024Article
- Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs-A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.Dento maxillo facial radiology · 2024Article
- Epidemiology of Anatomical Variations in the Maxillary Sinus Drainage System and the Impact of Concha Bullosa : Maxillary Sinus Drainage System and the Impact of Concha Bullosa.Galen medical journal · 2024Article
- The Application of Deep Learning on CBCT in Dentistry.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs (PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.
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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.