ArticleScientific reports2023
SinusC-Net for automatic classification of surgical plans for maxillary sinus augmentation using a 3D distance-guided network.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Deep learning in the diagnosis of maxillary sinus diseases: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Effectiveness of a Virtual Simulation Training System for Learning Lateral Approach Maxillary Sinus Floor Elevation Skill Among Dental Students: A Pilot Study.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Trial
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Coordinate attention and deformable convolution-enhanced YOLO for bilateral maxillary sinus segmentation, a paranasal sinus structure, in CT images.Frontiers in medicine · 2026Article
- NeuroNasal: Advanced AI-Driven Self-Supervised Learning Approach for Enhanced Sinonasal Pathology Detection.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
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Abstract
The objective of this study was to automatically classify surgical plans for maxillary sinus floor augmentation in implant placement at the maxillary posterior edentulous region using a 3D distance-guided network on CBCT images. We applied a modified ABC classification method consisting of five surgical approaches for the deep learning model. The proposed deep learning model (SinusC-Net) consisted of two stages of detection and classification according to the modified classification method. In detection, five landmarks on CBCT images were automatically detected using a volumetric regression network; in classification, the CBCT images were automatically classified as to the five surgical approaches using a 3D distance-guided network. The mean MRE for landmark detection was 0.87 mm, and SDR for 2 mm or lower, 95.47%. The mean accuracy, sensitivity, specificity, and AUC for classification by the SinusC-Net were 0.97, 0.92, 0.98, and 0.95, respectively. The deep learning model using 3D distance-guidance demonstrated accurate detection of 3D anatomical landmarks, and automatic and accurate classification of surgical approaches for sinus floor augmentation in implant placement at the maxillary posterior edentulous region.
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