Artificial Intelligence Designed Single Tooth Dental Prostheses

NCT ID: NCT05056948

Last Updated: 2025-10-03

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 250 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-09-01
• Study Completion Date: 2025-05-30

Brief 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.

Detailed 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.

Conditions

Dental Prosthesis

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: CROSS_SECTIONAL

Study Groups

Control

Original 3D maxillary teeth model from subjects who fulfill inclusion/exclusion criteria

No interventions assigned to this group

Test

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

artificial intelligence (AI) computer assisted design (CAD)

Intervention Type: OTHER

Maxillary right first molar will be removed in the computer and will be designed by artificial intelligence system

Interventions

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

Intervention Type: OTHER

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

University Grants Committee, Hong Kong

OTHER_GOV

Sponsor Role: collaborator

The University of Hong Kong

OTHER

Sponsor Role: lead

Responsible Party

Prof. Walter Y.H. Lam

Clinical Assistant Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Walter Lam, BDS, MDS

Role: PRINCIPAL_INVESTIGATOR

The University of Hong Kong

Locations

Prince Philip Dental Hospital

Sai Ying Pun, , Hong Kong

Countries

Hong Kong

References

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.

Reference Type: BACKGROUND

PMID: 3468151 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 3866768 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 27475920 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 28969919 (View on PubMed)

Other Identifiers

UW 20-848

Identifier Type: -

Identifier Source: org_study_id