Title
Authors
Affiliation
1Department of Dentistry, Nalanda Medical College Hospital, Agamkuan, Patna, Bihar, India; 2Luxmi Bai Institute of Dental Sciences and Hospital, Sirhind Road, Patiala, Punjab, India; 3Department of Periodontology, Dr. D. Y. Patil Dental College and Hospital, Pimpri, Pune, Maharashtra, India; 4Department of Public Health Dentistry, Late Shri Yashwantrao Chavan Memorial Medical & Rural Development Foundation’s Dental College & Hospital, Ahmednagar, Maharashtra, India; 5General Dentist, Smilebuilderz, Lancaster, Pennsylvania, United States; 6Programme Officer, Blood Cell, Commissionerate of Health and Family Welfare, Government of Telangana, Hyderabad, India; Corresponding author
Vijay Shekhar - E-mail: vjsspitzer@yahoo.in
Jaskiran Kaur Nain - E-mail: jasskirannain06@gmail.com
Gurmehak Kaur Sandhu - E-mail: gurmehaksandhu4@gmail.com
Vrushali Vasant Bhoir - E-mail: vrushalibhoir89@gmail.com
Sneha Das - E-mail: dassneha818@gmail.com
Yihan Fu - E-mail: annfu2010@gmail.com
Heena Dixit Tiwari - E-mail: drheenatiwari@gmail.com
Article Type
Research Article
Date
Received March 1, 2026; Revised March 31, 2026; Accepted March 31, 2026, Published March 31, 2026
Abstract
Visual shade selection in dentistry is inherently subjective and influenced by lighting conditions and examiner variability. Artificial intelligence (AI)-based shade matching systems provide objective, data-driven color analysis to improve consistency in aesthetic dentistry. This study compared AI-based shade matching with conventional visual assessment using a spectrophotometer as the reference standard. Sixty patients were evaluated using VITA (Vereinigte Internationale für Technologie in der Zahnmedizin) shade guides, AI software, and spectrophotometric measurements. AI achieved higher accuracy (86.7%) than visual assessment (58.3%) with statistical significance (p=0.031). Reproducibility was greater with AI (κ=0.78) compared to visual methods (κ=0.42). AI-based shade matching demonstrated superior accuracy and consistency, reducing examiner bias. Integration of AI in clinical practice may improve restorative outcomes, laboratory communication, and patient satisfaction
Keywords
Artificial intelligence, color perception, dental esthetics, tooth color, colorimetry
Citation
Shekhar et al. Bioinformation 22(3): 1306-1309 (2026)
Edited by
P Kangueane
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
