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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)

NCT07397546 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 268

Last updated 2026-02-10

No results posted yet for this study

Summary

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

Conditions

  • Shade Match
  • Shade Selection

Interventions

DIAGNOSTIC_TEST

Shade selection

The study will utilize digital spectrophotometry (VITA Easyshade®, Zahnfabrik H. Rauter GmbH \& Co. KG) as the reference standard, and an Artificial Intelligence-based shade selection system as the index test for determining the shade of maxillary anterior teeth in adult Egyptian patients. All enrolled participants will undergo both tests during the same visit to allow direct comparison between AI shade prediction and spectrophotometric measurement. The reference standard will be applied using a standardized clinical protocol because spectrophotometry is widely regarded as the instrumental "gold standard" for objective tooth color measurement in addition to objective color coordinates when available.

Sponsors & Collaborators

  • Cairo University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-03-01
Primary Completion
2026-05-01
Completion
2026-11-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07397546 on ClinicalTrials.gov