This system could help speed confirmation of PICC position and further be generalized to include other types of vascular access and therapeutic support devices” Lee et al (2017).
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.
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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.
Reference:
Lee, H., Mansouri, M., Tajmir, S., Lev, M.H. and Do, S. (2017) A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection. Journal of Digital Imaging. October 5th. [epub ahead of print].
doi: 10.1007/s10278-017-0025-z.
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