Artificial Intelligence Versus Conventional Digital Design for Fixed Dental Prosthesis

October University for Modern Sciences and Arts

Status: Enrolling

Conditions: Accuracy of Artificial Intelligence in Fixed Dental Prosthesis Design

Treatments: Other: Conventional CAD-Based Fixed Dental Prosthesis Design; Other: Artificial Intelligence-Based Fixed Dental Prosthesis Design

Study type: Interventional

Funder types: Other

Identifiers

NCT07432165

REC-D 1216-5

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,000 estimated patients

Sex: All

Ages: 18 to 65 years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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1,000 participants in 2 patient groups

Human-Designed Fixed Dental Prosthesis (Conventional CAD)

Other group

Description:

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.

Treatment:

Other: Conventional CAD-Based Fixed Dental Prosthesis Design

AI-Designed Fixed Dental Prosthesis

Other group

Description:

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.

Treatment:

Other: Artificial Intelligence-Based Fixed Dental Prosthesis Design