NCT07462156

AI-Driven Digital Self-Assessment Framework for Preclinical Tooth Preparation

IntelliPrep: An AI-Driven Digital Self-Assessment Framework for Preclinical Tooth Preparation-A Randomized Controlled Trial

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 36 (estimated)
• Sponsor: Alexandria University · Academic / Other
• Sex: All
• Age: 18 Years – 20 Years
• Healthy volunteers: Not accepted

Summary

This study aims to compare traditional faculty-based assessment with two AI-assisted digital self-assessment software programs for evaluating tooth preparations for metal-ceramic crowns for undergraduate dental preclinical students at College of Dentistry El Alamein, AAST in terms of: (1) Accuracy of preparation outcomes, (2) Student learning outcomes over a training period.

Conditions

• Dental Education

Interventions

Type	Name	Description
OTHER	Non-metrology-grade digital group (NMG)	Students in NMG used a license-free 3D comparison workflow (Medit Link/Compare, Compare tool; Medit Compare v3.4.9; Medit) to superimpose the prepared-tooth scan (TT-STL) onto the unprepared reference scan (RTS-STL).
OTHER	metrology-grade digital group (MG)	Students in MG used metrology-grade 3D inspection software (Geomagic Control X v2018.1.1; 3D Systems)to superimpose TT-STL onto RTS-STL. Initial Alignment was performed followed by Best Fit Alignment (iterative closest point registration).
OTHER	Traditional group (TG)	Students in TG assessed reduction with a silicone putty index and a periodontal probe across the previously predefined regions. Feedback was provided by experienced instructors (≥5 years of clinical teaching experience) using the same regional assessment approach.

Timeline

• Start date: 2026-02-01
• Primary completion: 2026-03-25
• Completion: 2026-03-25
• First posted: 2026-03-10
• Last updated: 2026-03-10

Locations

1 site across 1 country: Egypt