Ignite Creation Date:
2024-05-06 @ 4:40 PM
Last Modification Date:
2024-10-26 @ 2:14 PM
Study NCT ID:
NCT05056948
Status:
RECRUITING
Last Update Posted:
2023-11-29
First Post:
2021-09-09
Brief Title:
Artificial Intelligence Designed Single Tooth Dental Prostheses
Sponsor:
The University of Hong Kong
Organization:
The University of Hong Kong
Study Overview
Official Title:
Artificial Intelligence in Prosthodontics - Design of Maxillary Single-tooth Dental Prostheses
Status:
RECRUITING
Status Verified Date:
2023-11
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Tooth loss is common and as consequence deteriorate patients 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 methodsalgorithms 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 methodsalgorithms
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
1 To compare four deep-learning methodsalgorithms 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 methodsalgorithms
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None