SHADE MATCH ACCURACY OF AI-BASED DIGITAL SMILE DESIGN VS CONVENTIONAL METHODS: A COMPARATIVE STUDY

Background:Accurate tooth shade selection is critical for esthetic success. Conventional visual matching (with
shade guides and spectrophotometer support) is sensitive to lighting and operator variability. Artificialintelligence–assisted digital smile design (AI-DSD) may improve accuracy and efficiency by standardizing
image capture and shade mapping to CIEDE2000 (ΔE00_{00}00) thresholds.
Materials And Methods:Prospective, parallel-group comparative study (1:1 allocation) including adults
requiring a single anterior ceramic restoration. The AI-DSD group used standardized cross-polarized
photographs and an AI shade-classification pipeline; the conventional group used visual selection with VITA
3D-Master guided by a spectrophotometer. The primary outcome was shade-match accuracy at try-in, defined
as ΔE00_{00}00 ≤ 1.8 versus the natural reference tooth measured with bench spectroradiometry. Secondary
outcomes were mean ΔE00_{00}00, selection time, need for shade adjustment (staining/remake), inter-method
agreement (weighted κ), and repeatability. Two cal
Conclusions:Eighty participants were analyzed (40 per arm). AI-DSD increased the proportion of clinically
acceptable matches (85.0% vs 70.0%; risk difference 15.0%, 95% CI 0.7%–29.3%) and reduced mean color
difference (1.42 ± 0.56 vs 1.88 ± 0.72 ΔE00_{00}00; mean difference −0.46, 95% CI −0.76 to −0.16).
Chairside selection time was shorter (2.9 ± 0.8 vs 4.6 ± 1.2 minutes), with fewer shade adjustments (10.0% vs
22.5%). Agreement between pre-op selection and final crown verification was higher with AI-DSD (weighted κ
0.82 vs 0.68), and repeatability improved. AI-DSD offers a practical enhancement to conventional workflows,
shifting more cases into the clinically acceptable color range while improving efficiency.