Evaluation of Tooth Shade Selection With Conventional Methods and Artificial Intelligence-Integrated Methods: A Comparative Study.

Introduction Accurate tooth shade selection is fundamental for achieving optimal esthetic outcomes in restorative and prosthodontic dentistry. Conventional visual shade selection is widely used, but is susceptible to subjective variability arising from examiner experience, lighting conditions, and color perception. Artificial intelligence (AI)-integrated shade selection has emerged as a promising alternative, offering objective and reproducible color assessments. This study compared the diagnostic performance of conventional visual and AI-integrated tooth shade selection methods, using spectrophotometric evaluation as the reference standard. Materials and methods This prospective, single-center study included 55 participants who required tooth-shade assessment. Each participant underwent conventional visual shade selection using the VITA Classical A1-D4 and VITA Toothguide 3D-MASTER shade guides (VITA Zahnfabrik H. Rauter GmbH & Co. KG, Bad Säckingen, Germany), AI-assisted shade selection from standardized clinical photographs, and spectrophotometric evaluation using VITA Easyshade V (VITA Zahnfabrik H. Rauter GmbH & Co. KG, Bad Säckingen, Germany) as the reference standard. Agreement was assessed using Cohen's and weighted kappa statistics, color differences using CIEDE2000 (ΔE00), reliability using the intraclass correlation coefficient (ICC), and diagnostic performance using sensitivity, specificity, receiver operating characteristic (ROC) analysis, and area under the curve (AUC). Results The AI-integrated method demonstrated substantially higher agreement with the spectrophotometric reference than the conventional visual method (Cohen's κ: 0.75 vs. 0.43), with weighted κ values of 0.99 and 0.97, respectively. The exact shade agreement was 78.2% for the AI and 49.1% for the conventional visual assessment. The AI method exhibited excellent test-retest reliability (ICC 0.97), whereas intra- and inter-examiner ICCs for the conventional method were 0.71 and 0.74, respectively. The mean ΔE00 values were significantly lower for AI (1.42 ± 1.25) than for conventional visual assessment (1.76 ± 1.44), with a significant mean difference of -0.34 (95% CI: -0.57, -0.11; p = 0.020). AI achieved a higher diagnostic accuracy than the conventional method for detecting clinically perceptible shade mismatch (accuracy: 87.3% vs. 74.5%), with a sensitivity of 94.3% and specificity of 75.0%. ROC analysis demonstrated a superior discriminative ability for AI (AUC 0.932) compared to conventional visual assessment (AUC 0.895). Conclusions Artificial intelligence-integrated tooth shade selection demonstrated superior agreement, color accuracy, reliability, and diagnostic performance compared to conventional visual assessment when validated against spectrophotometric measurements. AI-assisted shade selection may serve as a reliable adjunct for objective color matching, improving esthetic predictability and consistency in restorative and prosthodontic practices. Larger multicenter studies are warranted to validate these findings and support broader clinical implementation.