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 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.
['Lee|Hyunkwang|H|', 'Mansouri|Mohammad|M|', 'Tajmir|Shahein|S|', 'Lev|Michael H|MH|', 'Do|Synho|S|']
D002405:Catheterization, Central Venous D002406:Catheterization, Peripheral D062905:Central Venous Catheters D016208:Databases, Factual D000077321:Deep Learning D004562:Electrocardiography D005260:Female D006801:Humans D008297:Male D016571:Neural Networks, Computer D061214:Patient Safety D010363:Pattern Recognition, Automated D013902:Radiography, Thoracic D012189:Retrospective Studies
PICC Chest radiograph Computer-aided detection Deep learning Machine learning Radiology workflow