ArticleDento maxillo facial radiology2024
Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.
Article in Dento maxillo facial radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Incidence and Associated Factors of Schneiderian Membrane Perforation in Maxillary Sinus Floor Elevation: A Meta-Analysis.International dental journal · 2026Pooled 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
- Deep learning in the diagnosis of maxillary sinus diseases: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Effectiveness of Artificial Intelligence in the Automatic Diagnosis of Lesions Detected in Panoramic Radiographs.Journal of imaging informatics in medicine · 2026Article
- CBCT assessment of maxillary sinus sexual dimorphism: morphometry and sex-classification performance in Peruvian adults.The Journal of forensic odonto-stomatology · 2026Observational
- Fully automated 3D segmentation of periapical lesions on CBCT images using nnU-Net v2.BMC medical imaging · 2026Article
- Deep learning-based automatic segmentation of MRONJ lesions on CBCT images.BMC oral health · 2026Article
- Development of a Preliminary Diagnostic Tool for the Segmentation of Benign Jaw Lesions in CBCT Images Using nnU-Net v2: An Artificial Intelligence-Based Approach.Journal of imaging informatics in medicine · 2026Article
- Automatic recognition and measurement of anatomical structures associated with the elevation of the maxillary sinus floor by deep learning on cone-beam computed tomographic scans.BMC oral health · 2026Article
- Deep learning-based 3D automatic segmentation of impacted canines in CBCT scans.BMC oral health · 2025Article
- Development of an Open-Source Algorithm for Automated Segmentation in Clinician-Led Paranasal Sinus Radiologic Research.The Laryngoscope · 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
- AI-powered segmentation of bifid mandibular canals using CBCT.BMC oral health · 2025Article
- Accuracy of deep learning models in the detection of accessory ostium in coronal cone beam computed tomographic images.Scientific reports · 2025Article
- A platform combining automatic segmentation and automatic measurement of the maxillary sinus and adjacent structures.Clinical oral investigations · 2025Article
- Automatic Segmentation of the Nasolacrimal Canal: Application of the nnU-Net v2 Model in CBCT Imaging.Journal of clinical medicine · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.
methodsIn 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.
resultsF1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.
conclusionsModels based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.
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