Clinical trial NCT05056948: Artificial Intelligence Designed Single Tooth Dental Prostheses
Official title: Artificial Intelligence in Prosthodontics - Design of Maxillary Single-tooth Dental Prostheses
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Dental Prosthesis
Keywords: Artificial Intelligence
Machine Learning
Deep Neural Network
Generative Adversarial Network (GAN)
Biomimetics
Dental Prostheses
Prosthodontic Treatment
Lead sponsor: The University of Hong Kong
Sponsor class: OTHER
Collaborators: name: University Grants Committee, Hong Kong
First posted: 2021-09-27
Enrollment: 250
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
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

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.

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.

Primary outcomes:
measure: 3D position of tooth
timeFrame: Outcome will be measured when 25% of training models were studied by AI, up to 6 months
measure: 3D position of tooth
timeFrame: Outcome will be measured when 50% of training models were studied by AI, up to 12 months
measure: 3D position of tooth
timeFrame: Outcome will be measured when 75% of training models were studied by AI, up to 18 months
measure: 3D position of tooth
timeFrame: Outcome will be measured after the whole training, which AI was trained of 100% of all models, up to 24 months
measure: Occlusal morphology of tooth
timeFrame: Outcome will be measured when 25% of training models were studied by AI, up to 6 months
measure: Occlusal morphology of tooth
timeFrame: Outcome will be measured when 50% of training models were studied by AI, up to 12 months
measure: Occlusal morphology of tooth
timeFrame: Outcome will be measured when 75% of training models were studied by AI, upto 18 months
measure: Occlusal morphology of tooth
timeFrame: Outcome will be measured after the whole training, which AI was trained of 100% of all models, upto 24 months
measure: Time spent in laboratory design and in clinical deliver of denture prostheses
timeFrame: Outcome will be measured after the whole training, which AI was trained of 100% of all models, upto 24 months
Secondary outcomes:
Other outcomes:
Locations:
city: Sai Ying Pun
country: Hong Kong
facility: Prince Philip Dental Hospital
Study officials:
affiliation: The University of Hong Kong
name: Walter Lam, BDS, MDS
role: PRINCIPAL_INVESTIGATOR
References:
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.
citation: 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.
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.
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.