ArticleBMC oral health2024
Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Incidence and Associated Factors of Schneiderian Membrane Perforation in Maxillary Sinus Floor Elevation: A Meta-Analysis.International dental journal · 2026Pooled it
- Automatic Detection of Sinus Membrane Thickness Using a Hybrid Deep Learning Model Integrating Multiple Attention Mechanisms.Journal of clinical medicine · 2026Article
- Comparative Evaluation of AI-Assisted and Manual CBCT-Derived Graft Volume Measurements for Maxillary Sinus Floor Augmentation.Diagnostics (Basel, Switzerland) · 2026Article
- PanMB2-Net: a deep learning framework for preoperative MB2 canal risk assessment on panoramic radiographs.BMC oral health · 2026Article
- Detection of maxillary sinusitis of endodontic origin in cone-beam CT images using deep learning algorithms.Scientific reports · 2026Article
- Radiologic Evaluation of Odontogenic Sinusitis and Its Etiologic Factors: Lessons Learned from a Retrospective Study with a Proposed Imaging-Guided Management Pathway.Journal of clinical medicine · 2026Article
- CBCT assessment of maxillary sinus sexual dimorphism: morphometry and sex-classification performance in Peruvian adults.The Journal of forensic odonto-stomatology · 2026Observational
- Automated detection and classification of maxillary sinus variations using slice-based and full-volume CBCT deep learning models.BMC oral health · 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
- Segmentation-Guided Preprocessing Improves Deep Learning Diagnostic Accuracy and Confidence of Ameloblastoma and Odontogenic Keratocyst in Cone Beam CT Images-A Preliminary Study.Diagnostics (Basel, Switzerland) · 2026Article
- Coordinate attention and deformable convolution-enhanced YOLO for bilateral maxillary sinus segmentation, a paranasal sinus structure, in CT images.Frontiers in medicine · 2026Article
- A deep learning based automated maxillary sinus segmentation and bone grafts analysis in CBCT images.NPJ digital medicine · 2025Article
- Review
- Accuracy of deep learning models in the detection of accessory ostium in coronal cone beam computed tomographic images.Scientific reports · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundMaxillofacial complex automated segmentation could alternative traditional segmentation methods to increase the effectiveness of virtual workloads. The use of DL systems in the detection of maxillary sinus and pathologies will both facilitate the work of physicians and be a support mechanism before the planned surgeries.
objectiveThe aim was to use a modified You Only Look Oncev5x (YOLOv5x) architecture with transfer learning capabilities to segment both maxillary sinuses and maxillary sinus diseases on Cone-Beam Computed Tomographic (CBCT) images.
methodsData set consists of 307 anonymised CBCT images of patients (173 women and 134 males) obtained from the radiology archive of the Department of Oral and Maxillofacial Radiology. Bilateral maxillary sinuses CBCT scans were used to identify mucous retention cysts (MRC), mucosal thickenings (MT), total and partial opacifications, and healthy maxillary sinuses without any radiological features.
resultsRecall, precision and F1 score values for total maxillary sinus segmentation were 1, 0.985 and 0.992, respectively; 1, 0.931 and 0.964 for healthy maxillary sinus segmentation; 0.858, 0.923 and 0.889 for MT segmentation; 0.977, 0.877 and 0.924 for MRC segmentation; 1, 0.942 and 0.970 for sinusitis segmentation.
conclusionThis study demonstrates that maxillary sinuses can be segmented, and maxillary sinus diseases can be accurately detected using the AI model.
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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.