Optimizing Catheter Verification: An Understandable AI Model for Efficient Assessment of Central Venous Catheter Placement in Chest Radiography.

Journal: Investigative radiology
PMID: 39724590

Abstract

PURPOSE: Accurate detection of central venous catheter (CVC) misplacement is crucial for patient safety and effective treatment. Existing artificial intelligence (AI) often grapple with the limitations of label inaccuracies and output interpretations that lack clinician-friendly comprehensibility. This study aims to introduce an approach that employs segmentation of support material and anatomy to enhance the precision and comprehensibility of CVC misplacement detection.

Authors

  • Jonas Stroeder
    From the Department of Radiology and Nuclear Medicine, UKSH Lübeck, Lübeck, Germany (J.S., M.M., L.B., Y.E., J.B., M.M.S.); Institute of Medical Informatics, University of Lübeck, Lübeck, Germany (L.H., M.P.H.); Philips Research Hamburg, Hamburg, Germany (A.S., H.S.); and Institute of Interventional Radiology, UKSH Lübeck, Lübeck, Germany (M.M.S.).
  • Malte Multusch
  • Lennart Berkel
  • Lasse Hansen
    Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany. hansen@imi.uni-luebeck.de.
  • Axel Saalbach
    Philips Research, Hamburg, Germany.
  • Heinrich Schulz
    Philips Research, 22335 Hamburg, Germany.
  • Mattias P Heinrich
    Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany. heinrich@imi.uni-luebeck.de.
  • Yannic Elser
  • Jörg Barkhausen
    Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany.
  • Malte Maria Sieren
    Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany. malte.sieren@uksh.de.

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