ArticleScientific reports2022
Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images.
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 54 papers, 1 of them a synthesis that pooled it.
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Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparisons of artificial intelligence automated segmentation techniques to manual segmentation techniques of the maxilla and maxillary sinus for CT or cone-beam CT scans-a systematic review.Dento maxillo facial radiology · 2025Pooled it
- A novel fluoride tray-assisted workflow for artificial intelligence-driven automated maxillary gingival segmentation on cone beam CT.Dento maxillo facial radiology · 2026Article
- Morphometric Analysis of the Maxillary Sinus Using CT-Based Air-Cavity Reconstruction: A Retrospective Study.Journal of clinical medicine · 2026Article
- Clinical Applicability of Artificial Intelligence-Driven Implant Planning and Surgical Guide Design in the Maxillary Esthetic Zone: A Registry-Based Cohort Study.Clinical oral implants research · 2026Article
- Comparative Evaluation of AI-Assisted and Manual CBCT-Derived Graft Volume Measurements for Maxillary Sinus Floor Augmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Automating virtual dental implant planning: can artificial intelligence match clinical expertise?Dento maxillo facial radiology · 2026Article
- Weighted knowledge distillation for semi-supervised segmentation of maxillary sinus in panoramic X-ray images.Scientific reports · 2026Article
- Detection of maxillary sinusitis of endodontic origin in cone-beam CT images using deep learning algorithms.Scientific reports · 2026Article
- Generative Artificial Intelligence: Applications and Future Prospects in Dentistry.International dental journal · 2026Review
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Deep learning model for automated segmentation of sphenoid sinus and middle skull base structures in CBCT volumes using nnU-Net v2.Oral radiology · 2026Article
- A deep learning based automated maxillary sinus segmentation and bone grafts analysis in CBCT images.NPJ digital medicine · 2025Article
- Deep learning-based 3D automatic segmentation of impacted canines in CBCT scans.BMC oral health · 2025Article
- Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review.European endodontic journal · 2025Review
- Development of an Open-Source Algorithm for Automated Segmentation in Clinician-Led Paranasal Sinus Radiologic Research.The Laryngoscope · 2025Article
- Cross-Sectional Radiological and Reconstructive Anatomy of the Paranasal Sinuses in Normal Mesaticephalic Dogs.Veterinary medicine and science · 2025Article
- Deploying a novel deep learning framework for segmentation of specific anatomical structures on cone-beam CT.Oral radiology · 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
- Automated 3D segmentation of the hyoid bone in CBCT using nnU-Net v2: a retrospective study on model performance and potential clinical utility.BMC medical imaging · 2025Article
- Artificial Intelligence Versus Human Intelligence in Presurgical Implant Planning: A Preclinical Validation.Clinical oral implants research · 2025Article
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6 authors.
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Abstract
An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT) dataset. Recently, convolutional neural networks (CNNs) have proven to provide excellent performance in the field of 3D image analysis. Hence, this study developed and validated a novel automated CNN-based methodology for the segmentation of maxillary sinus using CBCT images. A dataset of 264 sinuses were acquired from 2 CBCT devices and randomly divided into 3 subsets: training, validation, and testing. A 3D U-Net architecture CNN model was developed and compared to semi-automatic segmentation in terms of time, accuracy, and consistency. The average time was significantly reduced (p-value < 2.2e-16) by automatic segmentation (0.4 min) compared to semi-automatic segmentation (60.8 min). The model accurately identified the segmented region with a dice similarity co-efficient (DSC) of 98.4%. The inter-observer reliability for minor refinement of automatic segmentation showed an excellent DSC of 99.6%. The proposed CNN model provided a time-efficient, precise, and consistent automatic segmentation which could allow an accurate generation of 3D models for diagnosis and virtual treatment planning.
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