Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

Yu, Dingding; Zhang, Kaijie; Huang, Lingyan; Zhao, Bonan; Zhang, Xiaoshan; Guo, Xin; Li, Miaomiao; Gu, Zhongwei; Fu, Guosheng; Hu, Minchun; Ping, Yan; Ye, Sheng; Liu, Zhenjie; Hu, Xianliang; Zhao, Rui

doi:10.1016/j.cmpb.2020.105674

Cited by 22 publications

(10 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have been performed for the detection of lines and tubes in chest radiographs [44][45][46][47][48][49]. Generally the algorithms are quite good in classifying the presence versus absence of an endotracheal tube [50], but the systems perform worse when the exact position of the tip of the tube is sought.…”

Section: Lines/tubesmentioning

confidence: 99%

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

2021

View full text Add to dashboard Cite

Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications.

show abstract

Section: Lines/tubesmentioning

confidence: 99%

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

2021

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…Recent studies utilizing deep convolutional neural networks (CNNs) have described the CVC on chest radiography images [ 18 , 19 , 20 , 21 , 22 , 23 ]. Among them, two studies showed that the network for segmentation can increase the effectiveness of CVC tip detection and type classification [ 18 , 19 ]. Only one study demonstrated that deep learning using the National Institute of Health (NIH) ChestXRay14 dataset could classify the catheter malposition (normal, abnormal, and borderline) [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Central Venous Catheter Tip Position on Chest X-ray Using Artificial Intelligence

Jung

Ryu

et al. 2022

JPM

View full text Add to dashboard Cite

Recent studies utilizing deep convolutional neural networks (CNN) have described the central venous catheter (CVC) on chest radiography images. However, there have been no studies for the classification of the CVC tip position with a definite criterion on the chest radiograph. This study aimed to develop an algorithm for the automatic classification of proper depth with the application of automatic segmentation of the trachea and the CVC on chest radiographs using a deep CNN. This was a retrospective study that used plain chest supine anteroposterior radiographs. The trachea and CVC were segmented on images and three labels (shallow, proper, and deep position) were assigned based on the vertical distance between the tracheal carina and CVC tip. We used a two-stage approach model for the automatic segmentation of the trachea and CVC with U-net++ and automatic classification of CVC placement with EfficientNet B4. The primary outcome was a successful three-label classification through five-fold validations with segmented images and a test with segmentation-free images. Of a total of 808 images, 207 images were manually segmented and the overall accuracy of the five-fold validation for the classification of three-class labels (mean (SD)) of five-fold validation was 0.76 (0.03). In the test for classification with 601 segmentation-free images, the average accuracy, precision, recall, and F1-score were 0.82, 0.73, 0.73, and 0.73, respectively. We achieved the highest accuracy value of 0.91 in the shallow position label, while the highest F1-score was 0.82 in the deep position label. A deep CNN can achieve a comparative performance in the classification of the CVC position based on the distance from the carina to the CVC tip as well as automatic segmentation of the trachea and CVC on plain chest radiographs.

show abstract

“…Peripherally inserted central catheters (PICCs) have been widely applied for the administration of chemotherapy drugs ( 1-3 ). PICCs have the advantages of convenient operation, high safety, long indwelling time and low maintenance difficulty ( 4 ), and can provide patients with intravenous treatment for 7 days to 1 year, which is beneficial to reduce damage to blood vessels and avoid patient suffering associated with repeated punctures ( 5 ). Furthermore, PICC provides good venous access for nutrition and chemotherapy in patients with cancer ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

Construction of a rabbit model with vinorelbine administration via peripherally inserted central catheter and dynamic monitoring of changes in phlebitis and thrombosis

Huang

Chen

Hu³

et al. 2022

Exp Ther Med

View full text Add to dashboard Cite

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

Cited by 22 publications

References 16 publications

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

Classification of Central Venous Catheter Tip Position on Chest X-ray Using Artificial Intelligence

Construction of a rabbit model with vinorelbine administration via peripherally inserted central catheter and dynamic monitoring of changes in phlebitis and thrombosis

Contact Info

Product

Resources

About