A I in the Prediction of Clinical Performance, Marginal Fit and Fracture Resistance of Vertical Versus Horizontal Margin Designs Fabricated With 2 Ceramic Materials

NCT06164002 | Active Not Recruiting

• 1 other identifier: P-CR-23-04
• Study Type: interventional
• Target: 40
• Locations: 1 country
• Sites: 1

Brief Summary

The present study will be performed to evaluate application of artificial intelligence in the prediction of clinical performance, marginal fit and fracture resistance of vertical "feather-edge" versus horizontal "shoulder" margin designs fabricated with two ceramic materials (zirconia and hybrid ceramic)

Conditions

Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• Artificial intelligence application assessment, laboratory marginal fit and fracture resistance

  Predicting the follow up results using the new (artificial intelligence "AI") model for vertical "feather-edge" versus horizontal "shoulder" margin designs fabricated with two ceramic materials (zirconia and hybrid ceramic) that saves the clinical evaluation period, hence saves the expenses of the dental clinic.

  6 months

Secondary Outcomes (3)

• In-Vitro Study preparations.

  6 months

• In-Vitro Study

  6 months

• In-Vivo Study

  Baseline,3 months,6 months

Study Arms (4)

Group (I)

EXPERIMENTAL

Patients will be prepared with vertical "feather edge" margin design.

Other: change in restoration material and margin design.

Group (II)

EXPERIMENTAL

Patients will be prepared with horizontal "shoulder" margin design.

Other: change in restoration material and margin design.

Subgroup (A)

EXPERIMENTAL

Patients will receive zirconia ceramic crowns

Other: change in restoration material and margin design.

Subgroup (B)

EXPERIMENTAL

Patients will receive hybrid ceramic crowns.

Other: change in restoration material and margin design.

Interventions

change in restoration material and margin design. OTHER

comparing two different materials and two different designs. use artificial intelligence in the prediction of follow up results

Also known as: Predicting the follow up results using the new (artificial intelligence "AI") model

Group (I); Group (II); Subgroup (A); Subgroup (B)

Eligibility Criteria

• Age: 20 Years - 50 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)

You may qualify if:

• \. Age range of patients should be 20-50 years old, able to read and sign the informed consent document. 2. Patient with posterior molars indicated for full coverage restorations. 3. Patient should be able to physically and psychologically tolerate conventional restorative procedures. 4. Patients who are willing to return for follow-up examination and evaluation. 5. Indicated molar should have adequate tooth preparation length to ensure proper retention and resistance form. 6. Patient with sound contralateral or adjacent tooth to the selected tooth requiring full coverage restoration.

You may not qualify if:

• Patient in the growth stage with partially erupted teeth.
• Patient with poor oral hygiene and motivation.
• Pregnant women.
• Patient with psychiatric problems or unrealistic expectations.
• Patients with no opposing occluding dentition in the area intended for restoration.
• Patients with parafunctional habits. -

Sponsors & Collaborators

• Faculty of Dental Medicine for Girls: lead

Study Sites (1)

Walaa Tarek El Hefnawy

Cairo, Al-Azhar University, 02, Egypt

Location

MeSH Terms

Interventions

Artificial Intelligence; Models, Biological

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts; Models, Theoretical; Investigative Techniques

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER GOV
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: assisstant lecturer

Study Record Dates

• First Submitted: August 16, 2023
• First Posted: December 11, 2023
• Study Start: September 1, 2023
• Primary Completion: September 15, 2024
• Study Completion: May 15, 2025
• Last Updated: February 6, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

EG (1)