Artificial Intelligence Versus Conventional Digital Design for Fixed Dental Prosthesis
Accuracy Assessment of Artificial Intelligence Versus Conventional Digital Design for Fixed Dental Prosthesis: (An Invitro Study)
This in vitro study aims to evaluate the accuracy of an Artificial Intelligence (AI)-based automatic design system for fixed dental prosthesis (FDP) compared with conventional computer-aided design (CAD) software. Digital scans of teeth requiring fixed dental prosthesis will be collected and used to generate prosthetic designs using two approaches: human-designed CAD restorations and AI-generated restorations.
The primary outcome is design accuracy assessed using 3D superimposition and Intersection over Union (IOU) percentage. Secondary outcomes include margin detection performance measured using F1 score, precision, and recall. A total sample size of 438 scans will be analyzed.
The study will determine whether AI-generated prosthesis designs demonstrate comparable accuracy to conventional digital designs.
Study Overview
Status
Detailed Description
This study is designed as an in vitro comparative study to assess the accuracy and performance of an Artificial Intelligence (AI)-based automatic design system for fixed dental prosthesis (FDP) in comparison with conventional computer-aided design (CAD) software.
Digital scans of patients requiring fixed dental prosthesis will be collected from the production laboratory of the Faculty of Dentistry. Eligible scans will include adults aged 18-65 years with damaged teeth requiring FDP and adequate occlusal anatomy for analysis.
The AI workflow consists of three sequential phases: training (60%), validation (10%), and testing (30%). The AI model will be trained using natural spatial tooth morphology and historical human-designed FDP datasets. The conventional group will consist of FDPs manually designed by experienced dental professionals using CAD software.
Primary Outcome:
The primary outcome is crown design accuracy measured using 3D superimposition analysis and quantified using Intersection over Union (IOU) percentage.
Secondary Outcome:
Margin detection accuracy will be assessed using F1 score, precision, and recall metrics.
Statistical analysis will be performed using MedCalc software (Version 22). Continuous variables will be presented as mean, root mean square, and standard deviation. Comparisons between groups will be conducted using paired t-test with a significance level set at P ≤ 0.05 (two-tailed).
The null hypothesis states that there will be no statistically significant difference between AI-designed and human-designed fixed dental prostheses.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Locations
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Giza, Egypt
- Recruiting
- MSA university
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Contact:
- Modern Sciences and Arts MSA October University for Modern Sciences and Arts
- Phone Number: 002-0216672
- Email: info@msa.edu.eg
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Adults aged 18-65 years Patients with a damaged tooth requiring a fixed dental prosthesis Available digital intraoral scans Adequate occlusal anatomy for analysis of opposing teeth
Exclusion Criteria:
- Incomplete or poor-quality digital scans Severe occlusal abnormalities affecting analysis Patients outside the specified age range
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
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Other: Human-Designed Fixed Dental Prosthesis (Conventional CAD)
Fixed dental prostheses will be designed manually using conventional CAD software by experienced dental professionals based on occlusal anatomy and patient-specific scan data.
The designs will serve as the control comparator to evaluate accuracy against AI-generated designs using 3D superimposition analysis.
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Fixed dental prostheses will be digitally designed using conventional computer-aided design (CAD) software by experienced dental professionals.
Designs will be based on occlusal anatomy and patient-specific intraoral scan data.
These manually generated digital designs will serve as the comparator for evaluating accuracy against AI-generated designs using 3D superimposition and quantitative accuracy analysis.
Other Names:
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Other: AI-Designed Fixed Dental Prosthesis
Fixed dental prostheses will be automatically generated using an artificial intelligence-based design system.
The AI model will be trained, validated, and tested using occlusal scan datasets and historical human-designed prostheses.
The generated designs will be evaluated for accuracy using 3D superimposition and Intersection over Union (IoU) analysis.
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An artificial intelligence-based automated design system will generate fixed dental prosthesis designs using deep learning algorithms.
The AI model will be trained (60%), validated (10%), and tested (30%) on occlusal scan datasets and historical human-designed prostheses.
Generated designs will be evaluated for accuracy and marginal precision using 3D superimposition and Intersection over Union (IoU) analysis.
Other Names:
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Accuracy of AI-Designed Fixed Dental Prosthesis Compared to Human-Designed Prosthesis
Time Frame: Immediately after crown design generation (at time of digital analysis)
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Accuracy will be assessed by superimposing AI-generated crown designs and human-designed crowns using 3D imaging software.
The Intersection over Union (IOU) percentage will be calculated to evaluate morphological agreement and occlusal fit between the two design approaches.
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Immediately after crown design generation (at time of digital analysis)
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Margin Detection Performance of AI System
Time Frame: Immediately after digital crown design generation
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Margin detection accuracy will be evaluated using F1 score, precision, and recall metrics to assess the AI model's ability to accurately identify preparation margins compared to human-designed reference models.
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Immediately after digital crown design generation
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Collaborators and Investigators
Publications and helpful links
General Publications
- Wu Z et al., 2024 Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study. International Dental Journal. 2024.
- Ding H et al., 2023 Morphology and mechanical performance of dental crown designed by 3D-DCGAN. Dental Materials. 2023.
- Chau RCW et al., 2024 Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study. Journal of Prosthetic Dentistry. 2024.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Other Study ID Numbers
- REC-D 1216-5
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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