Generation and application of a convolutional neural networks algorithm in evaluating stool consistency in diapers.

Journal: Acta paediatrica (Oslo, Norway : 1992)
PMID: 36843346

Abstract

AIM: The aim of the study was to develop a deep convolutional neural networks (CNNs) algorithm for automated assessment of stool consistency from diaper photographs and test its performance under real-world conditions.

Authors

  • Fangfei Xiao
    Department of Gastroenterology, Hepatology, and Nutrition, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Yizhong Wang
    Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
  • Thomas Ludwig
    Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore.
  • Xiaolu Li
    Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
  • Sijia Chen
    Danone Open Science Research Center, Shanghai, China.
  • Nan Sun
    Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Yixiao Zheng
    Danone Open Science Research Center, Shanghai, China.
  • Koen Huysentruyt
    KidZ Health Castle, UZ Brussel, Vrije Universiteit Brussel, Brussels, Belgium.
  • Yvan Vandenplas
    KidZ Health Castle, UZ Brussel, Vrije Universiteit Brussel, Brussels, Belgium.
  • Ting Zhang
    Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Institute of Pediatrics, Beijing 100020, China.

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