Photographic LVAD Driveline Wound Infection Recognition Using Deep Learning

Noël Lüneburg, Nils Reiss, Christina Feldmann, Pim van der Meulen, Michiel van de Steeg, Thomas Schmidt, Regina Wendl, Sybren Jansen

Publication: Contribution to journalConference article in journalResearchpeer-review

Abstract

The steady increase in the number of patients equipped with mechanical heart support implants, such as left ventricular assist devices (LVAD), along with virtually ubiquitous 24/7 internet connectivity coverage is motive to investigate and develop remote patient monitoring. In this study we explore machine learning approaches to infection severity recognition on driveline exit site images. We apply a U-net convolutional neural network (CNN) for driveline tube segmentation, resulting in a Dice score coefficient of 0.95. A classification CNN is trained to predict the membership of one out of three infection classes in photographs. The resulting accuracy of 67% in total is close to the measured expert level performance, which indicates that also for human experts there may not be enough information present in the photographs for accurate assessment. We suggest the inclusion of thermographic image data in order to better resolve mild and severe infections.

Original languageEnglish
JournalStudies in health technology and informatics
Volume260
Pages (from-to)192-199
Number of pages8
ISSN0926-9630
Publication statusPublished - 2019

Research areas and keywords

  • Deep Learning
  • Heart-Assist Devices/adverse effects
  • Humans
  • Neural Networks, Computer
  • Prosthesis-Related Infections
  • Wound Infection/diagnosis

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