Evaluation of Color Changes in Composite Resins Using Artificial Intelligence-Based Digital Photography

Abstract

This study aimed to compare color measurements of composite resins obtained using a spectrophotometer and a digital camera, and to evaluate the feasibility of an image-based classification model developed using Google Cloud Vertex Artificial Intelligence (AI) AutoML. Filtek Z350 XT (A2) specimens were immersed in a coffee solution at 37°C for 1, 3, or 5 hours, and CIE 1976 L*a*b* (CIELAB) color coordinates and ΔE*ab(stain) were recorded before and after immersion. Two AutoML models (VITA shade guide classification and ΔE*ab(stain) classification) were trained using standardized images. Digital photography yielded significantly lower a* values than spectrophotometry in all groups, whereas b* values did not differ significantly. Mean ΔE*ab(inter) values between instruments remained below 2.7 in all groups, indicating clinically acceptable agreement. Both AI models achieved an accuracy of 93.3%, exhibiting stable performance in identifying specimens with ΔE*ab(inter) < 2.7. Despite systematic bias in a* measurements, AI-assisted digital photography provided clinically acceptable color assessment under controlled conditions, supporting its potential as an adjunctive tool to spectrophotometric evaluation.