ArticleScientific reports2024
Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Developing an Artificial Intelligence Solution to Autosegment the Edentulous Maxillary Bone for Implant Planning.European journal of dentistry · 2026Article
- Comparison of artificial intelligence methods with manual measurements for mandibular bucco-lingual bone width assessment using cone-beam computed tomography images.Oral radiology · 2026Article
- Development of a preoperative accuracy prediction model using machine learning for implant placement in static-guided surgery: retrospective observational study.International journal of implant dentistry · 2026Observational
- The Use of Artificial Intelligence in Planning Dental Implant Procedures: A Systematic Review.Dentistry journal · 2026Review
- Adults' dental cone beam computed tomography images dataset for detecting and classifying missing teeth.Scientific data · 2026Article
- Detection and Classification of Peri-Implant Marginal Bone Loss in Cone-Beam Computed Tomography Using a Deep Learning Approach.Clinical and experimental dental research · 2026Article
- A Deep Metric Learning and Multimodal Gated Fusion Framework for AI-Driven Risk Assessment of Lingual Plate Perforation and Mandibular Canal Injury in Posterior Mandible Implants.International journal of dentistry · 2026Article
- The Segmentation and 3D Reconstruction of Teeth, Root Canals, and Alveolar Bones in CBCT Images Based on AVIZO Software.International journal of dentistry · 2026Article
- Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry.Dentistry journal · 2024Review
- Data-Efficient Bone Segmentation Using Feature Pyramid- Based SegFormer.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
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Abstract
Recent studies have shown that dental implants have high long-term survival rates, indicating their effectiveness compared to other treatments. However, there is still a concern regarding treatment failure. Deep learning methods, specifically U-Net models, have been effectively applied to analyze medical and dental images. This study aims to utilize U-Net models to segment bone in regions where teeth are missing in cone-beam computerized tomography (CBCT) scans and predict the positions of implants. The proposed models were applied to a CBCT dataset of Taibah University Dental Hospital (TUDH) patients between 2018 and 2023. They were evaluated using different performance metrics and validated by a domain expert. The experimental results demonstrated outstanding performance in terms of dice, precision, and recall for bone segmentation (0.93, 0.94, and 0.93, respectively) with a low volume error (0.01). The proposed models offer promising automated dental implant planning for dental implantologists.
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