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A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection.

A peripherally inserted central catheter (PICC) is a thin catheter that is inserted via arm veins and threaded near the heart, providing intravenous access. The final catheter tip position is always confirmed on a chest radiograph (CXR) immediately after insertion since malpositioned PICCs can cause...

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Published in:Journal of Digital Imaging Vol. 31; no. 4; pp. 393 - 403
Main Authors: Lee, Hyunkwang, Mansouri, Mohammad, Tajmir, Shahein, Lev, Michael H., Do, Synho
Format: diagnostic images research tables/charts Journal Article
Published: Springer Nature Aug2018
Online Access:View this record in EBSCOhost
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      dt: Aug2018
      vid: 31
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-017-0025-z
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        atl: A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection.
      aug:
        au:
          Lee, Hyunkwang
          Mansouri, Mohammad
          Tajmir, Shahein
          Lev, Michael H.
          Do, Synho
        affil: Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Suite 400B, 02114, Boston, MA, USA
      sug:
        subj:
          Machine Learning Methods
          Automation
          Peripherally Inserted Central Catheters
          Radiography, Thoracic
          Neural Networks (Computer)
          Image Interpretation, Computer Assisted
          Descriptive Statistics
      ab: A peripherally inserted central catheter (PICC) is a thin catheter that is inserted via arm veins and threaded near the heart, providing intravenous access. The final catheter tip position is always confirmed on a chest radiograph (CXR) immediately after insertion since malpositioned PICCs can cause potentially life-threatening complications. Although radiologists interpret PICC tip location with high accuracy, delays in interpretation can be significant. In this study, we proposed a fully-automated, deep-learning system with a cascading segmentation AI system containing two fully convolutional neural networks for detecting a PICC line and its tip location. A preprocessing module performed image quality and dimension normalization, and a post-processing module found the PICC tip accurately by pruning false positives. Our best model, trained on 400 training cases and selectively tuned on 50 validation cases, obtained absolute distances from ground truth with a mean of 3.10 mm, a standard deviation of 2.03 mm, and a root mean squares error (RMSE) of 3.71 mm on 150 held-out test cases. This system could help speed confirmation of PICC position and further be generalized to include other types of vascular access and therapeutic support devices.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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