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NCT Number: NCT07462156

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

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.

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Key information

Conditions

Age range

18 year–20 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

College of Dentistry El Alamein - AAST

El Alamein, Egypt

Location status: Recruiting

Location contact

Mahinour Yousry, PhD

CONTACT

[email protected]

Mahinour Yousry, PhD

PRINCIPAL_INVESTIGATOR

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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 criteria

  • previous repetition of the course
  • Substantial prior experience with digital metrology/3D inspection software
  • Inability to attend all scheduled training and examination sessions.

Treatment and study plan

Non-metrology-grade digital group (NMG)

Other

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)

Other

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)

Other

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.

Primary outcomes

  1. Change in preparation scores

    Time frame: Up to 5 weeks

    Preparations were scored with a prespecified 10-item rubric derived from ADEX criteria for mandibular first molar metal-ceramic crown preparation (overall score 0-10). Each item was rated on a 3-level scale (0, 0.5, or 1) and summed to obtain a total score

  2. RMS deviation from the ideal preparation

    Time frame: Up to 5 weeks

    all the resulting TT-STL files across all sessions (from 1-5) of the study of the three groups of prepared teeth were compared with the ideal preparation

Study contacts

Contact information is provided by the study sponsor or research team.

Mahinour Yousry, PhD

CONTACT

[email protected]

+2 01060080088

Sponsors and collaborators

Lead sponsor

Alexandria University

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Mar 10, 2026
Registry last updated
Mar 10, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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