PurposeTo integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice.ApproachIn clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built on the SimpleMind Cognitive AI platform and integrated into a clinical workflow. It automatically identified the ETT and checked its placement relative to the trachea and carina. The ETT overlay and misplacement alert messages generated by the AI system were compared with radiology reports as the reference. A survey study was also conducted to evaluate usefulness of the AI system in clinical practice.ResultsThe alert messages indicating that either the ETT was misplaced or not detected had a positive predictive value of 42% (21/50) and negative predictive value of 98% (161/164) based on the radiology reports. In the survey, radiologist and ICU physician users indicated that they agreed with the AI outputs and that they were useful.ConclusionsThe AI system performance in real-world clinical use was comparable to that seen in previous experiments. Based on this and physician survey results, the system can be deployed more widely at our institution, using insights gained from this evaluation to make further algorithm improvements and quality assurance of the AI system.
Deep convolutional neural networks (CNNs) are widely used in medical image segmentation, but they tend to ignore smaller foreground classes in the training process and thus degrade segmentation accuracy, which is known as the class imbalance problem. Central venous catheter (CVC) segmentation suffers from such problems, leading to low accuracy. The purpose of this study is to address the class imbalance problem in CNN training for segmenting the right internal jugular lines (RIJLs), the most common type of CVCs, in chest X-ray (CXR) images. We applied an inverse class frequency weight to the standard Dice loss to formulate a class frequency weighted Dice loss (CFDL) function to train the CNNs. A U-net based segmentation model was constructed with multichannel pre-processing, including normalization, denoising, and histogram equalization, and post-processing including thresholding segmentation candidates and interpolating discontinued line segmentations. The segmentation model was trained on CXR images with the Dice loss and the CFDL respectively. A separate test set of images were used to evaluate the CNN output performances with the Dice Similarity Coefficient (DSC). Between the Dice loss and the CFDL, the CFDL-trained CNN generated segmentation results with a mean DSC of 0.581 on the separate test set, which indicates a statistically significant difference (p=0.001) from the Dice loss-trained CNN outputs with a mean DSC of 0.562. The inverse class frequency weighted Dice loss function improved the RIJL segmentation with a U-net to the state-of-the-art performance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.