Using deep learning-derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data.

Journal: European radiology
Published Date: Jun 7, 2023

Abstract

OBJECTIVES: Siamese neural networks (SNN) were used to classify the presence of radiopaque beads as part of a colonic transit time study (CTS). The SNN output was then used as a feature in a time series model to predict progression through a CTS.

Authors

  • Brendan S Kelly
    St Vincent's University Hospital, Dublin, Ireland. brendanskelly@me.com.
  • Prateek Mathur
    Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
  • Jan Plesniar
    School of Medicine, University College Dublin, Dublin, Ireland.
  • Aonghus Lawlor
    Insight SFI Centre for Data Analytics, University College Dublin, Dublin, Ireland.
  • Ronan P Killeen
    St Vincent's University Hospital, Dublin, Ireland.

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