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

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Abstract

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.

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Acknowledgements

The authors gratefully acknowledge Jordan Rogers for his commitment to initial implementation of PICC line detection machine learning system. We thank Junghwan Cho, PhD; Dania Daye, MD; Vishala Mishra, MD; and Garry Choy, MD for their helpful clinical comments.

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Authors and Affiliations

  1. Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA

    Hyunkwang Lee, Mohammad Mansouri, Shahein Tajmir, Michael H. Lev & Synho Do

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  1. Hyunkwang Lee
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  2. Mohammad Mansouri
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  3. Shahein Tajmir
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  4. Michael H. Lev
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  5. Synho Do
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Correspondence to Synho Do.

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Lee, H., Mansouri, M., Tajmir, S. et al. A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection. J Digit Imaging 31, 393–402 (2018). https://doi.org/10.1007/s10278-017-0025-z

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