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Artificial Intelligence Versus Conventional Digital Design for Fixed Dental Prosthesis

NCT07432165 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 1000

Last updated 2026-02-25

No results posted yet for this study

Summary

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.

Conditions

  • Accuracy of Artificial Intelligence in Fixed Dental Prosthesis Design

Interventions

OTHER

Conventional CAD-Based Fixed Dental Prosthesis Design

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

Artificial Intelligence-Based Fixed Dental Prosthesis Design

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.

Sponsors & Collaborators

  • October University for Modern Sciences and Arts

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-06-15
Primary Completion
2026-04-02
Completion
2026-06-17

Countries

  • Egypt

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07432165 on ClinicalTrials.gov