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
Alexandria University
36 participants
Feb 1, 2026
INTERVENTIONAL
Conditions
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.
Eligibility
Inclusion Criteria3
- Third-year preclinical dental student
- Completion of the fixed prosthodontics tooth-preparation course.
- No prior formal training or use of digital 3D tooth-preparation assessment software.
Exclusion Criteria3
- previous repetition of the course
- Substantial prior experience with digital metrology/3D inspection software
- Inability to attend all scheduled training and examination sessions.
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Interventions
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).
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).
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)
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NCT07462156