Achieving an accurate shade match is a critical factor in the success of anterior esthetic restorations, directly influencing patient satisfaction, perceived treatment success, and long-term acceptance of restorations. Tooth color is a complex, multidimensional phenomenon influenced by hue, chroma, value, translucency and surface texture, and small discrepancies can be easily perceived in the esthetic zone.
Traditionally, shade selection has been performed visually using commercial shade guides such as the VITA Classical or VITA 3D-Master systems. However, visual shade matching is inherently subjective and is significantly affected by examiner experience, training, surrounding environment, light source, observer fatigue, and metamerism. Several studies have shown that visual methods demonstrate only mild-to-moderate reliability and agreement, even among trained clinicians and students.
To overcome these limitations, digital spectrophotometers were introduced to provide objective, reproducible, CIELAB-based color measurements of natural teeth and restorations. These devices analyze reflected light within a defined wavelength range and express the tooth shade within established systems such as VITA Classical A1-D4 and VITA 3D- Master. They have been widely used as an instrumental "gold standard" against which visual shade selection is evaluated, consistently demonstrating higher accuracy and better repeatability than conventional visual methods.
More recently, artificial intelligence (AI) and machine learning (ML) approaches have been explored for dental shade matching. Deep learning models based on convolutional neural networks and other ML algorithms can analyze standardized intraoral photographs or smartphone images to automatically classify tooth shades according to VITA shade systems, often showing promising accuracy, precision and F1-scores, comparable to or sometimes exceeding experienced clinicians.
In vitro studies have started to compare AI-based shade matching applications with spectrophotometers and image-based photometric analysis, suggesting that although spectrophotometers still tend to provide the most accurate color match, AI systems are rapidly improving and may offer clinically acceptable results with advantages in speed, usability, and integration into digital workflows. However, most of these investigations have been conducted using laboratory setups, artificial teeth, or non-Egyptian populations, and there remains a scarcity of in vivo diagnostic-accuracy studies validating AI shade selection systems against an accepted instrumental standard in real clinical settings