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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05056948
Other study ID # UW 20-848
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date September 1, 2021
Est. completion date May 2025

Study information

Verified date November 2023
Source The University of Hong Kong
Contact Walter Lam, BDS, MDS
Phone +852-2859-0306
Email retlaw@hku.hk
Is FDA regulated No
Health authority
Study type Observational

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.


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.


Recruitment information / eligibility

Status Recruiting
Enrollment 250
Est. completion date May 2025
Est. primary completion date September 2024
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - Subjects with sufficient dentition present for the determination of the upper occlusal plane - Subjects with more than 12 occluding pairs and stable intercuspal position - Subjects with teeth restorations that did not grossly alter its morphology - Subjects who did not undergo orthodontic treatment and/or did not have teeth that rotated more than 45 degrees and/or displaced more than 1.5 mm - Subjects who are of Cantonese descent. Exclusion Criteria: - Subjects with periodontal disease whereby there is pathological tooth migration and alteration of occlusal plane. - Subjects who are under the age of 18 and unable to give consent. - Subjects with extensive teeth restorations that affect the morphology.

Study Design


Related Conditions & MeSH terms


Intervention

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

Locations

Country Name City State
Hong Kong Prince Philip Dental Hospital Sai Ying Pun

Sponsors (2)

Lead Sponsor Collaborator
The University of Hong Kong University Grants Committee, Hong Kong

Country where clinical trial is conducted

Hong Kong, 

References & Publications (4)

Chow TW, Clark RK, Cooke MS. Errors in mounting maxillary casts using face-bow records as a result of an anatomical variation. J Dent. 1985 Dec;13(4):277-82. doi: 10.1016/0300-5712(85)90021-1. No abstract available. — View Citation

Chow TW, Clark RK, Cooke MS. The orientation of the occlusal plane in Cantonese patients. J Dent. 1986 Dec;14(6):262-5. doi: 10.1016/0300-5712(86)90034-5. No abstract available. — View Citation

Lam WY, Hsung RT, Choi WW, Luk HW, Pow EH. A 2-part facebow for CAD-CAM dentistry. J Prosthet Dent. 2016 Dec;116(6):843-847. doi: 10.1016/j.prosdent.2016.05.013. Epub 2016 Jul 28. — View Citation

Lam WYH, Hsung RTC, Choi WWS, Luk HWK, Cheng LYY, Pow EHN. A clinical technique for virtual articulator mounting with natural head position by using calibrated stereophotogrammetry. J Prosthet Dent. 2018 Jun;119(6):902-908. doi: 10.1016/j.prosdent.2017.07.026. Epub 2017 Sep 29. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary 3D position of tooth The center of a tooth automatically determined by computer Outcome will be measured when 25% of training models were studied by AI, up to 6 months
Primary 3D position of tooth The center of a tooth automatically determined by computer Outcome will be measured when 50% of training models were studied by AI, up to 12 months
Primary 3D position of tooth The center of a tooth automatically determined by computer Outcome will be measured when 75% of training models were studied by AI, up to 18 months
Primary 3D position of tooth The center of a tooth automatically determined by computer Outcome will be measured after the whole training, which AI was trained of 100% of all models, up to 24 months
Primary Occlusal morphology of tooth The cusps (highest point) and the fossa (lowest point) of the occlusal surface Outcome will be measured when 25% of training models were studied by AI, up to 6 months
Primary Occlusal morphology of tooth The cusps (highest point) and the fossa (lowest point) of the occlusal surface Outcome will be measured when 50% of training models were studied by AI, up to 12 months
Primary Occlusal morphology of tooth The cusps (highest point) and the fossa (lowest point) of the occlusal surface Outcome will be measured when 75% of training models were studied by AI, upto 18 months
Primary Occlusal morphology of tooth The cusps (highest point) and the fossa (lowest point) of the occlusal surface Outcome will be measured after the whole training, which AI was trained of 100% of all models, upto 24 months
Primary 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 Outcome will be measured after the whole training, which AI was trained of 100% of all models, upto 24 months
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