ArticleScientific reports2016
An ontology-driven, case-based clinical decision support model for removable partial denture design.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Application, performance and limitations of artificial intelligence for the diagnosis and prediction of oro-facial pain: a systematic review.Frontiers in oral health · 2026Pooled it
- The Potential of Artificial Intelligence in Prosthodontics: A Comprehensive Review.Medical science monitor : international medical journal of experimental and clinical research · 2024Pooled it
- The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Artificial intelligence and the future of prosthodontics: a narrative review.The Saudi dental journal · 2026Article
- Preliminary Evaluation of Large Language Models in Kennedy Classification and Removable Partial Denture Planning: An In Silico Study.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Automated design prediction for definitive obturator prostheses: A case-based reasoning study.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025Article
- Integration of Artificial Intelligence in Dentistry: A Systematic Review of Educational and Clinical Implications.Cureus · 2025Review
- Deep Learning Architecture to Infer Kennedy Classification of Partially Edentulous Arches Using Object Detection Techniques and Piecewise Annotations.International dental journal · 2025Article
- Biomechanical considerations in RPD design: application and perspective of finite element method in distal extension removable partial denture rehabilitation.Frontiers in dental medicine · 2025Review
- The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study.BMC oral health · 2024Article
- Efficacy of a Virtual 3D Simulation-Based Digital Training Module for Building Dental Technology Students' Long-Term Competency in Removable Partial Denture Design: Prospective Cohort Study.JMIR serious games · 2024Article
- Artificial intelligence in prosthodontics.Bioinformation · 2024Article
- A brief exploration of artificial intelligence in dental healthcare: a narrative review.F1000Research · 2024Review
- The Use of Artificial Intelligence in Dentistry Practices.The Eurasian journal of medicine · 2022Article
- Current state of dental informatics in the field of health information systems: a scoping review.BMC oral health · 2022Article
- Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls.Diagnostics (Basel, Switzerland) · 2022Review
- [A technique to design the framework of removable partial denture by multi-stage expert system].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2020Article
- Methods for a similarity measure for clinical attributes based on survival data analysis.BMC medical informatics and decision making · 2019Article
- Genetic similarity of biological samples to counter bio-hacking of DNA-sequencing functionality.Scientific reports · 2019Article
- Electronic Health Record Driven Prediction for Gestational Diabetes Mellitus in Early Pregnancy.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present the initial work toward developing a clinical decision support model for specific design of removable partial dentures (RPDs) in dentistry. We developed an ontological paradigm to represent knowledge of a patient's oral conditions and denture component parts. During the case-based reasoning process, a cosine similarity algorithm was applied to calculate similarity values between input patients and standard ontology cases. A group of designs from the most similar cases were output as the final results. To evaluate this model, the output designs of RPDs for 104 randomly selected patients were compared with those selected by professionals. An area under the curve of the receiver operating characteristic (AUC-ROC) was created by plotting true-positive rates against the false-positive rate at various threshold settings. The precision at position 5 of the retrieved cases was 0.67 and at the top of the curve it was 0.96, both of which are very high. The mean average of precision (MAP) was 0.61 and the normalized discounted cumulative gain (NDCG) was 0.74 both of which confirmed the efficient performance of our model. All the metrics demonstrated the efficiency of our model. This methodology merits further research development to match clinical applications for designing RPDs. This paper is organized as follows. After the introduction and description of the basis for the paper, the evaluation and results are presented in Section 2. Section 3 provides a discussion of the methodology and results. Section 4 describes the details of the ontology, similarity algorithm, and application.
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