Machine Learning Supports Automated Digital Image Scoring of Stool Consistency in Diapers.

Journal: Journal of pediatric gastroenterology and nutrition
Published Date:

Abstract

BACKGROUND/AIMS: Accurate stool consistency classification of non-toilet-trained children remains challenging. This study evaluated the feasibility of automated classification of stool consistencies from diaper photos using machine learning (ML).

Authors

  • Thomas Ludwig
    Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore.
  • Ines Oukid
    Danone Research, Palaiseau, France.
  • Jill Wong
    Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore.
  • Steven Ting
    Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore.
  • Koen Huysentruyt
    KidZ Health Castle, UZ Brussel, Vrije Universiteit Brussel, Brussels, Belgium.
  • Puspita Roy
    Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore.
  • Agathe C Foussat
    Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore.
  • Yvan Vandenplas
    KidZ Health Castle, UZ Brussel, Vrije Universiteit Brussel, Brussels, Belgium.