Dental Prosthesis Clinical Trial
Official title:
Artificial Intelligence in Prosthodontics - Design of Maxillary Single-tooth Dental Prostheses
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 |
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.
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. |
Country | Name | City | State |
---|---|---|---|
Hong Kong | Prince Philip Dental Hospital | Sai Ying Pun |
Lead Sponsor | Collaborator |
---|---|
The University of Hong Kong | University Grants Committee, Hong Kong |
Hong Kong,
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
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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