Artificial Intelligence Designed Single Tooth Dental Prostheses

NCT05056948 | Completed DMC

• 1 other identifier: UW 20-848
• Study Type: observational
• Target: 250
• Locations: 1 country
• Sites: 1

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.

Conditions

Dental Prosthesis

Keywords

Artificial Intelligence; Machine Learning; Deep Neural Network; Generative Adversarial Network (GAN); Biomimetics; Dental Prostheses; Prosthodontic Treatment

Outcome Measures

Primary Outcomes (9)

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

Study Arms (2)

Control

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

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

Other: artificial intelligence (AI) computer assisted design (CAD)

Interventions

artificial intelligence (AI) computer assisted design (CAD) OTHER

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

Test

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

* Patients attended/attending Prince Philip Dental Hospital * Dental undergraduate students from the Faculty of Dentistry, The University of Hong Kong

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• The University of Hong Kong: lead
• University Grants Committee, Hong Kong: collaborator

Study Sites (1)

Prince Philip Dental Hospital

Sai Ying Pun, Hong Kong

Location

Related Publications (4)

• 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.

  PMID: 3468151 BACKGROUND

• 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.

  PMID: 3866768 BACKGROUND

• 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.

  PMID: 27475920 BACKGROUND

• 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.

  PMID: 28969919 BACKGROUND

MeSH Terms

Interventions

Artificial Intelligence

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Walter Lam, BDS, MDS

  The University of Hong Kong

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Assistant Professor

Study Record Dates

• First Submitted: September 9, 2021
• First Posted: September 27, 2021
• Study Start: September 1, 2021
• Primary Completion: September 1, 2024
• Study Completion: May 30, 2025
• Last Updated: October 3, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share

Locations

HK (1)