Letter to the Editor
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Nov 16, 2024; 12(32): 6559-6562
Published online Nov 16, 2024. doi: 10.12998/wjcc.v12.i32.6559
Classification and detection of dental images using meta-learning
Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Mario Alberto Alarcón-Sánchez, Artak Heboyan
Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Department of Periodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, Tamil Nadu, India
Mario Alberto Alarcón-Sánchez, South Pacific Dental Institute, Chilpancingo de los Bravo 39022, Guerrero, Mexico
Artak Heboyan, Department of Prosthodontics, Faculty of Stomatology, Yerevan State Medical University after Mkhitar Heratsi, Yerevan 0025, Armenia
Author contributions: Yadalam PK, Anegundi RV, Alarcón-Sánchez MA and Heboyan A contributed to this paper; Yadalam PK and Anegundi RV designed the overall concept and outline of the manuscript; Yadalam PK, Anegundi RV, Alarcón-Sánchez MA and Heboyan A contributed to the discussion and design of the manuscript; Yadalam PK, Anegundi RV, Alarcón-Sánchez MA and Heboyan A contributed to the writing, and editing the manuscript and review of literature.
Conflict-of-interest statement: All the authors declare that they have no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Artak Heboyan, DDS, MD, MSc, PhD, Associate Professor, Department of Prosthodontics, Faculty of Stomatology, Yerevan State Medical University after Mkhitar Heratsi, Yerevan 0025, Armenia. heboyan.artak@gmail.com
Received: February 6, 2024
Revised: August 25, 2024
Accepted: September 12, 2024
Published online: November 16, 2024
Processing time: 230 Days and 11.9 Hours
Core Tip

Core Tip: Meta-learning offers a promising approach for achieving high-accuracy detection and diagnosis in dental radiographic image classification. This method holds significant promise for accurate and reliable prediction with less bias by leveraging its capability to learn from limited training data and generalize effectively to unseen dental X-ray categories.