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Accuracy Comparison of New Photo-Analysis Software and Digital Spectrophotometer with Visual Method for Resin Composite Shade Matching
Led by Misr University for Science and Technology · Updated on 2026-07-16
52
Participants Needed
1
Research Sites
4 weeks
Total Duration
AI-Summary
What this Trial Is About
Researchers are evaluating new computer software that analyzes dental photographs, a digital spectrophotometer, and traditional visual techniques to improve accuracy in matching resin composite shades for dental restorations. The study focuses on comparing these methods to determine how well they agree and how accurately they select the correct shade. This diagnostic study uses specially fabricated composite resin shade tabs as reference standards to ensure precise shade matching. Participants will have their teeth professionally cleaned to remove stains and then positioned under standardized daylight conditions for shade assessment. The study employs a novel photo-analysis software that processes photos taken with a Nikon DSLR camera to analyze tooth color, a digital spectrophotometer that measures color values independently, and experienced assessors who perform visual shade matching. Each methods shade selection is compared using color difference calculations to identify the closest match. During the study, participants will sit in a controlled environment while undergoing shade matching using the three different methods. Researchers will measure accuracy by comparing the shade matches from the software, spectrophotometer, and visual assessments. The study includes randomization and crossover design, with assessments performed under standardized conditions to minimize external influences. Participation will last from screening through assessments during daylight, with results analyzed for agreement and accuracy.
CONDITIONS
Brief Title
Diagnostic Accuracy of Innovative Photo-Analysis Software in Resin Composite Shade Matching
Research Team
A
Ahmed Saleh Ali ASA Resident doctor, Bachelor degree
E
Esraa Esmeail Hussien EEH lecturer, phd holder
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