ArticleClinical oral investigations2025
A platform combining automatic segmentation and automatic measurement of the maxillary sinus and adjacent structures.
Article in Clinical oral investigations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Automatic Detection of Sinus Membrane Thickness Using a Hybrid Deep Learning Model Integrating Multiple Attention Mechanisms.Journal of clinical medicine · 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
- External sinus lift with simultaneous implant placement for severely atrophic maxilla: optimization of staged surgical approaches.BDJ open · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
objectivesTo develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters. MATERIALS AND
methods175 CBCTs containing 242 MS were used as the training, validating and testing datasets at the ratio of 7:1:2. The datasets contained healthy MS and MS with mild (2-4 mm), moderate (4-10 mm) and severe (10- mm) mucosal thickening. A DCNN algorithm adopting 2.5D structure was trained for automatic segmentation. Automatic measuring algorithms were further developed to evaluate the clinical reliability of the DCNN.
resultsThe median Dice Similarity Coefficient (DSC) for the air cavity, mucosa, teeth and maxillary bone segmentation were 0.990, 0.850, 0.961 and 0.953, respectively. The Intra-class Correlation Coefficien (ICC) of all automatic measuring algorithms exceeded 0.975. The 95% confidence interval (95%CI) of all volumetric metric bias were within ± 0.5 cm
conclusionsThe DCNN provided clinically reliable results. The automatic measuring algorithms could reveal 3D information embedded in CBCT 2D planes on the basis of automatic segmentation. CLINICAL RELEVANCE: This platform helps dentists to conduct instant 3D reconstruction and automatic measuring of 3D clinical parameters of MS and adjacent structures.
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