Early childhood caries risk prediction using machine learning approaches in Bangladesh.

Journal: BMC oral health
PMID: 39780148

Abstract

BACKGROUND: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifying crucial health behaviours within mother-child pairs.

Authors

  • Fardous Hasan
    Department of Clinical Dentistry, Faculty of Medicine, University of Bergen, Bergen, Norway.
  • Maha El Tantawi
    Department of Pediatric Dentistry and Dental Public Health, Faculty of Dentistry, Alexandria University, Alexandria, Egypt.
  • Farzana Haque
    Department of Clinical Dentistry, Faculty of Medicine, University of Bergen, Bergen, Norway.
  • Moréniké Oluwátóyìn Foláyan
    Early Childhood Caries Advocacy Group, University of Manitoba, Winnipeg, Canada.
  • Jorma I Virtanen
    Department of Clinical Dentistry, Faculty of Medicine, University of Bergen, Bergen, Norway. jorma.virtanen@uib.no.

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