ArticleBioinformation2026
AI-based shade matching versus visual assessment: Accuracy in dental aesthetics.
Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
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Abstract
Visual shade selection in dentistry is inherently subjective and influenced by lighting conditions and examiner variability. Artificial intelligence (AI)-based shade matching systems provide objective, data-driven color analysis to improve consistency in aesthetic dentistry. This study compared AI-based shade matching with conventional visual assessment using a spectrophotometer as the reference standard. Sixty patients were evaluated using VITA (Vereinigte Internationale für Technologie in der Zahnmedizin) shade guides, AI software, and spectrophotometric measurements. AI achieved higher accuracy (86.7%) than visual assessment (58.3%) with statistical significance (p=0.031). Reproducibility was greater with AI (κ=0.78) compared to visual methods (κ=0.42). AI-based shade matching demonstrated superior accuracy and consistency, reducing examiner bias. Integration of AI in clinical practice may improve restorative outcomes, laboratory communication, and patient satisfaction.
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