AI-Driven Digital Self-Assessment Framework for Preclinical Tooth Preparation
NCT07462156 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 36
Last updated 2026-03-10
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
- 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.
Sponsors & Collaborators
-
Alexandria University
lead OTHER
Study Design
- Allocation
- RANDOMIZED
- Purpose
- OTHER
- Masking
- SINGLE
- Model
- PARALLEL
Eligibility
- Min Age
- 18 Years
- Max Age
- 20 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2026-02-01
- Primary Completion
- 2026-03-25
- Completion
- 2026-03-25
Countries
- Egypt
Study Locations
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