Clinical Research Directory

Evaluation of Different Color Determination Methods

With the increasing aesthetic demands in dental practice, the accuracy and consistency of tooth color determination methods have gained importance for clinical success. This study aims to compare different tooth color determination methods in terms of accuracy and repeatability under in vivo conditions. A spectrophotometric system (VITA Easyshade V) will be used as the reference method. In comparison, the results obtained through visual assessment by observers, a digital intraoral scanner (TRIOS 3), and artificial intelligence-based color analysis systems will be evaluated. Statistical analyses will be conducted based on ΔE, L\*, a\*, b\*, and shade code data obtained from the measurements, and the agreement among the methods as well as their correlations with the spectrophotometer will be examined. This study aims to demonstrate the clinical validity of digital and AI-assisted methods and to emphasize the importance of objective approaches in tooth shade selection. The aim of this study is to compare four different tooth color determination methods used in aesthetic dentistry-spectrophotometric system, observer-based visual assessment, digital intraoral scanner, and artificial intelligence-assisted analysis system-in terms of accuracy and repeatability under in vivo conditions. The study seeks to statistically evaluate and compare the color measurement values obtained from these methods with those of the spectrophotometric system, which is considered the reference method, and to determine the level of agreement among the methods. In addition, it aims to assess the clinical applicability and reliability of digital technologies and artificial intelligence-based systems in tooth color selection.

AI-Assisted Shade Selection Versus Digital Spectrophotometry in Determining Maxillary Anterior Tooth Color in a Group of Egyptian Patients at Cairo University, Faculty of Dentistry Hospital (Diagnostic Accuracy Study)

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