Clinical Research Directory
Browse clinical research sites, groups, and studies.
AI-Driven Digital Self-Assessment Framework for Preclinical Tooth Preparation
Sponsor: Alexandria University
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.
Official title: IntelliPrep: An AI-Driven Digital Self-Assessment Framework for Preclinical Tooth Preparation-A Randomized Controlled Trial
Key Details
Gender
All
Age Range
18 Years - 20 Years
Study Type
INTERVENTIONAL
Enrollment
36
Start Date
2026-02-01
Completion Date
2026-03-25
Last Updated
2026-03-10
Healthy Volunteers
No
Conditions
Interventions
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).
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).
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.
Locations (1)
College of Dentistry El Alamein - AAST
El Alamein, Egypt