Accuracy Assessment of Artificial Intelligence Versus Conventional Digital Design for Fixed Dental Prosthesis: (An Invitro Study)
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.
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.
Inclusion Criteria:
Exclusion Criteria: