Classification and detection of dental images using meta-learning

doi:10.12998/wjcc.v12.i32.6559

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Nov 16, 2024 (publication date) through Mar 5, 2026

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World Journal of Clinical Cases

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2307-8960

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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.

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.