Clinical Trials Logo

Clinical Trial Summary

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.


Clinical Trial Description

Objectives: 1. To compare four deep-learning methods/algorithms in interpreting and learning of the features of 3D models; 2. To compare the AI system with maxillary tooth model alone to maxillary and mandibular (antagonist) models; 3. To compare the occlusal morphology and 3D position of the single-tooth dental prostheses designed by trained AI and by dental technicians. 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. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05056948
Study type Observational
Source The University of Hong Kong
Contact Walter Lam, BDS, MDS
Phone +852-2859-0306
Email retlaw@hku.hk
Status Recruiting
Phase
Start date September 1, 2021
Completion date May 2025

See also
  Status Clinical Trial Phase
Recruiting NCT06114966 - Titanium Versus Soft Metal CAD/CAM Frameworks for All-on-4 Implant Supported Prosthesis N/A
Completed NCT05081050 - Short Implants Supporting Single Crowns in the Posterior Region
Completed NCT03146780 - Digital vs Conventional Impressions Study N/A
Recruiting NCT04600297 - 3 Years Clinical Evaluation of 3D Printed Resin Composite Fixed Dental Prosthesis N/A
Recruiting NCT05130996 - Observational Study Evaluating Safety and Performance of IDI Dental Implant Systems in Subjects Followed for 18 Months.
Completed NCT05153213 - Correlation of Length of Index Finger to Vertical Dimensions of Occlusion for Edentulous Patients N/A
Completed NCT03753932 - Impact of Fixed Dentures in Head and Neck Cancer (IMFDHAC) N/A
Enrolling by invitation NCT06406751 - Microbiome Effects of Extended Use of MI Paste in Elderly Removable Denture Wearers N/A
Completed NCT02758457 - Zirconia and Metal-based Single Crown Posterior Restorations. Phase 4