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Comparing Traditional and Digital Scanning Methods for Full-Arch Implant Dental Prostheses A Crossover Study on Accuracy and Fit in Edentulous Lower Jaws
Led by Mirna ashraf anis heneen · Updated on 2025-07-15
4
Participants Needed
1
Research Sites
N/A
Total Duration
AI-Summary
What this Trial Is About
Researchers are evaluating three different techniques for making impressions to create full-arch implant-supported dental prostheses. This study focuses on participants who have four well-integrated dental implants in the lower jaw. The goal is to compare conventional splinted impressions, standard digital scanning, and geometric pattern-assisted digital scanning to see which method provides the most accurate fit for the final prosthetic framework, which is important for implant success and patient comfort. Participants will experience all three impression methods in a randomized order. The conventional method uses splinted impression copings and polyvinyl siloxane PVS material on a custom tray. The standard digital scan uses an intraoral scanner with scan bodies attached to the implants. The geometric pattern-assisted digital scan adds a visual reference pattern to the digital scanning process to improve accuracy. Each impression produces a digital model, and prosthetic frameworks are made using CADCAM technology and 3D laser sintering. During the study, the accuracy of each impression technique is assessed by measuring the three-dimensional deviation between test and reference scans immediately after each impression. Clinical fit is also evaluated using a one-screw test within one hour of framework placement. Participants will attend all required appointments and provide feedback. The study will monitor the precision of these methods to help improve digital prosthodontic workflows and outcomes. Total participation spans the time needed to complete all three impression sessions and evaluations.
CONDITIONS
Brief Title
Accuracy of Standard and Geometric Pattern-Assisted Digital Scanning for Full-Arch Implant Prosthesis Frameworks
Research Team
M
Mirna A. Anis
K
Khaled M. Zahran
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