Dental ProsthesisNCT05056948
Artificial Intelligence Designed Single Tooth Dental Prostheses
An observational study of Dental Prosthesis, run by The University of Hong Kong at 1 site in Hong Kong.
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). Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Dental Prosthesis
- What is being tested?
- artificial intelligence (AI) computer assisted design (CAD)
- How many people
- 250 participants
- Where
- 1 siteHong Kong
- Study dates
- 1 Sep 2021to 30 May 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: * Patients attended/attending Prince Philip Dental Hospital * Dental undergraduate students from the Faculty of Dentistry, The University of Hong Kong
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Artificial Intelligence in Prosthodontics - Design of Maxillary Single-tooth Dental Prostheses
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.
Read the detailed description
Objectives:
- To compare four deep-learning methods/algorithms in interpreting and learning of the features of 3D models;
- To compare the AI system with maxillary tooth model alone to maxillary and mandibular (antagonist) models;
- 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
- Being tested
- artificial intelligence (AI) computer assisted design (CAD)
- Keywords
- Artificial Intelligence, Machine Learning, Deep Neural Network, Generative Adversarial Network (GAN), Biomimetics, Dental Prostheses, Prosthodontic Treatment
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: Other: artificial intelligence (AI) computer assisted design (CAD) |
What is being measured
The primary outcome is the main question the study is designed to answer.
3D position of tooth
The center of a tooth automatically determined by computer
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: Outcome will be measured after the whole training, which AI was trained of 100% of all models, upto 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Sep 2021
- Study start1 Sep 2021
- Primary completion1 Sep 2024
- Study completion30 May 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Dental ProsthesisStudies of the same condition, open and under way.
- More trials run by The University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 250 participants (actual)
Sponsor and investigators
- Lead sponsor
- The University of Hong Kong
- Sponsor type
- University, hospital or other
- Collaborators
- University Grants Committee, Hong Kong
- Responsible party
- Principal investigator, Prof. Walter Y.H. Lam, The University of Hong Kong
- Investigators
- Walter Lam, BDS, MDS, Principal investigator, The University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05056948
- Sponsor's protocol ID
- UW 20-848
- Status last verified
- Sep 2025
- First submitted
- 9 Sep 2021
- Last updated
- 3 Oct 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: There is no IPD sharing plan yet
Publications
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.
PubMed 3468151Chow 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.
PubMed 3866768Lam 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.
PubMed 27475920Lam 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.
PubMed 28969919
Study at a glance
From the registry record, last updated by the sponsor on 3 Oct 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence in Prosthodontics - Design of Maxillary Single-tooth Dental Prostheses
- ClinicalTrials.gov ID
- NCT05056948
- Acronym / sponsor ID
- UW 20-848
- Conditions
- Dental Prosthesis
- Keywords
- Artificial Intelligence, Machine Learning, Deep Neural Network, Generative Adversarial Network (GAN), Biomimetics, Dental Prostheses, Prosthodontic Treatment
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 250 participants (actual)
- Lead sponsor
- The University of Hong KongUniversity, hospital or other
- Collaborators
- University Grants Committee, Hong Kong
- Study start
- 1 Sep 2021 (actual)
- Primary completion
- 1 Sep 2024 (actual)
- Study completion
- 30 May 2025 (actual)
- First posted
- 27 Sep 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- To compare four deep-learning methods/algorithms in interpreting and learning of the features of 3D models;
- To compare the AI system with maxillary tooth model alone to maxillary and mandibular (antagonist) models;
- 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
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Control | – | –Original 3D maxillary teeth model from subjects who fulfill inclusion/exclusion criteria |
| Test | – | Other: artificial intelligence (AI) computer assisted design (CAD)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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- * Patients attended/attending Prince Philip Dental Hospital * Dental undergraduate students from the Faculty of Dentistry, The University of Hong Kong
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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 | Other | Test |
Primary outcome
3D position of tooth
Time frame: Outcome will be measured when 25% of training models were studied by AI, up to 6 months
The center of a tooth automatically determined by computer
3D position of tooth
Time frame: Outcome will be measured when 50% of training models were studied by AI, up to 12 months
The center of a tooth automatically determined by computer
3D position of tooth
Time frame: Outcome will be measured when 75% of training models were studied by AI, up to 18 months
The center of a tooth automatically determined by computer
3D position of tooth
Time frame: Outcome will be measured after the whole training, which AI was trained of 100% of all models, up to 24 months
The center of a tooth automatically determined by computer
Occlusal morphology of tooth
Time frame: Outcome will be measured when 25% of training models were studied by AI, up to 6 months
The cusps (highest point) and the fossa (lowest point) of the occlusal surface
Occlusal morphology of tooth
Time frame: Outcome will be measured when 50% of training models were studied by AI, up to 12 months
The cusps (highest point) and the fossa (lowest point) of the occlusal surface
Occlusal morphology of tooth
Time frame: Outcome will be measured when 75% of training models were studied by AI, upto 18 months
The cusps (highest point) and the fossa (lowest point) of the occlusal surface
Occlusal morphology of tooth
Time frame: Outcome will be measured after the whole training, which AI was trained of 100% of all models, upto 24 months
The cusps (highest point) and the fossa (lowest point) of the occlusal surface
Time spent in laboratory design and in clinical deliver of denture prostheses
Time frame: Outcome will be measured after the whole training, which AI was trained of 100% of all models, upto 24 months
Time (in minutes) spend in a) design and b) deliver of dental prostheses
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Oct 2025.
- Background: PubMed 3468151Chow 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.
- Background: PubMed 3866768Chow 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.
- Background: PubMed 27475920Lam 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.
- Background: PubMed 28969919Lam 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.
- Individual participant dataSharing planned: No. There is no IPD sharing plan yet
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Walter Lam, BDS, MDS principal investigator
The University of Hong Kong
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Dental Prosthesis, run by The University of Hong Kong at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 3 Oct 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
3D position of tooth (time frame: Outcome will be measured when 25% of training models were studied by AI, up to 6 months). The study lists 9 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05056948, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Oct 2025.