An observational study in Dental Prosthesis, sponsored by The University of Hong Kong. Completed at 1 site in Hong Kong. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-10-03.
Sponsored by The University of Hong Kong · Observational
Tooth loss is common and as consequence deteriorate patient's health and quality-of-life. Dental prostheses aim to restore patients' appearance and functions by replacement of missing teeth. The occlusal morphology and 3D position of the healthy natural teeth should be adopted by the dental prostheses (biomimetic). Despite computer-assisted design (CAD) software are available for designing dental prostheses, considerable clinical time are still required to fit the dental prostheses into patients' occlusion (teeth-to-teeth relationship). Teeth of an individual subjects are genetically controlled and exposed to mostly identical oral environment, therefore the occlusal morphology and 3D position of teeth are inter-related. It is hypothesized that artificial intelligence (AI) can automated designing the single-tooth dental prostheses from the features of remaining dentition.
Objectives:
Methods:
First, investigators will collect 200 maxillary dentate teeth models as training models. AI will learn the relationship between individual teeth and rest of the dentition using the 3D Generative Adversarial Network (GAN) by following deep-learning methods/algorithms:
Group 1) Voxel-based; Group 2) View-based; Group 3) Point-based; and Group 4) Fusion methods. Investigators will collect another 100 maxillary models that serve as validation models. Investigators will remove a tooth (act as control) in each model. Then investigators will evaluate these deep learning algorithms in predicting the occlusal morphology and 3D position of single-missing tooth.
Second, investigators will evaluate the need of antagonist model in predicting the occlusal morphology and 3D position of single-missing tooth in 100 validation models:
Group i) maxillary model only and Group ii) with antagonist model using the tested deep-learning algorithm in objective (1).
Third, investigators will analyze the geometric morphometric and 3D position of dental prostheses designed by:
Group a) the trained AI system; Group b) dental technicians on the physical models; and Group c) dental technicians using CAD software. Investigators will compare these teeth to the corresponding natural teeth (control) in 100 validation models.
Furthermore, investigators will analyze the time required for tooth design in these groups as secondary outcome.
The University of Hong Kong is the lead sponsor of 1,262 studies on the registry; 340 are open to participants now.
Of its 8 completed or terminated interventional studies of FDA-regulated products, 0 (0%) have results posted.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
Original 3D maxillary teeth model from subjects who fulfill inclusion/exclusion criteria
3D maxillary teeth model from subjects who fulfill inclusion/exclusion criteria. The right first molar (FDI number 16) will be removed in the computer and then designed by artificial intelligence (AI) system AI system will be trained by 1. different algorithms such as Group 1) Voxel-based; Group 2) View-based; Group 3) Point-based; and Group 4) Fusion methods 2. Group i) maxillary model only and Group ii) with antagonist model
Other: artificial intelligence (AI) computer assisted design (CAD)
Maxillary right first molar will be removed in the computer and will be designed by artificial intelligence system
3D position of tooth
The center of a tooth automatically determined by computer
Time frame: Outcome will be measured when 25% of training models were studied by AI, up to 6 months
3D position of tooth
The center of a tooth automatically determined by computer
Time frame: Outcome will be measured when 50% of training models were studied by AI, up to 12 months
3D position of tooth
The center of a tooth automatically determined by computer
Time frame: Outcome will be measured when 75% of training models were studied by AI, up to 18 months
3D position of tooth
The center of a tooth automatically determined by computer
Time frame: Outcome will be measured after the whole training, which AI was trained of 100% of all models, up to 24 months
Occlusal morphology of tooth
The cusps (highest point) and the fossa (lowest point) of the occlusal surface
Time frame: Outcome will be measured when 25% of training models were studied by AI, up to 6 months
Occlusal morphology of tooth
The cusps (highest point) and the fossa (lowest point) of the occlusal surface
Time frame: Outcome will be measured when 50% of training models were studied by AI, up to 12 months
Occlusal morphology of tooth
The cusps (highest point) and the fossa (lowest point) of the occlusal surface
Time frame: Outcome will be measured when 75% of training models were studied by AI, upto 18 months
Occlusal morphology of tooth
The cusps (highest point) and the fossa (lowest point) of the occlusal surface
Time frame: Outcome will be measured after the whole training, which AI was trained of 100% of all models, upto 24 months
Time spent in laboratory design and in clinical deliver of denture prostheses
Time (in minutes) spend in a) design and b) deliver of dental prostheses
Time frame: Outcome will be measured after the whole training, which AI was trained of 100% of all models, upto 24 months
Plan to share: No — There is no IPD sharing plan yet
This study is completed, as verified in Sep 2025. You cannot join it, but the record below documents what was studied.
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The University of Hong Kong