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

Gongbo Liang, Halemane Ganesh, Dylan Steffe, Liangliang Liu, Nathan Jacobs, Jie Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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. Method: We build a CNN model for feeding tube positioning assessment by pre-training the model under a weakly supervised fashion on large quantities of radiographs. Since most of the model was pre-trained, a small amount of labeled data is needed when fine-tuning the model for tube positioning assessment. We demonstrate the proposed method using a small dataset with 175 radiographs. Result: The experimental result shows that the proposed model improves the area under the receiver operating characteristic curve (AUC) by up to 35.71% , from 0.56 to 0.76, and 14.49% on the accuracy, from 0.69 to 0.79 when compared with the no pre-trained method. The proposed method also has up to 40% less error when estimating its prediction confidence. Conclusion: Our evaluation results show that the proposed model has a high prediction accuracy and a more accurate estimated prediction confidence when compared to the no pre-trained model and other baseline models. The proposed method can be potentially used for assessing the enteral tube positioning. It also provides a strong baseline for future studies.

Original languageEnglish
Article number52
JournalBMC Medical Imaging
Issue number1
StatePublished - Dec 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).


  • Annotation-efficient modeling
  • Pre-training
  • Weakly supervised

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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