Use machine learning to predict treatment outcome of early childhood caries.

Journal: BMC oral health
PMID: 40089762

Abstract

BACKGROUND: Early childhood caries (ECC) is a major oral health problem among preschool children that can significantly influence children's quality of life. Machine learning can accurately predict the treatment outcome but its use in ECC management is limited. The aim of this study is to explore the application of machine learning in predicting the treatment outcome of ECC.

Authors

  • Yafei Wu
    The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China.
  • Maoni Jia
    The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiang'an Nan Road, Xiang'an District, Xiamen, Fujian 361102, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, China.
  • Ya Fang
    The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China.
  • Duangporn Duangthip
    College of Dentistry, The Ohio State University, Columbus, U.S.A.
  • Chun Hung Chu
    Faculty of Dentistry, University of Hong Kong, Hong Kong, China.
  • Sherry Shiqian Gao
    Department of Stomatology, School of Medicine, Xiamen University, Xiamen, China. sherrysgao@xmu.edu.cn.

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