Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set.

Journal: BMC medical imaging
PMID: 35317725

Abstract

BACKGROUND: Enteral nutrition through feeding tubes serves as the primary method of nutritional supplementation for patients unable to feed themselves. Plain radiographs are routinely used to confirm the position of the Nasoenteric feeding tubes the following insertion and before the commencement of tube feeds. Convolutional neural networks (CNNs) have shown encouraging results in assisting the tube positioning assessment. However, robust CNNs are often trained using large amounts of manually annotated data, which challenges applying CNNs on enteral feeding tube positioning assessment.

Authors

  • Gongbo Liang
    Department of Computer Science, University of Kentucky, Lexington, Kentucky.
  • Halemane Ganesh
    University of Kentucky, Lexington, KY, USA.
  • Dylan Steffe
    University of Kentucky, Lexington, KY, USA.
  • Liangliang Liu
    College of Automation, Harbin Engineering University, Harbin 150001, China.
  • Nathan Jacobs
  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.

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