Neural Network Detection of Pacemakers for MRI Safety

Abstract

Flagging the presence of cardiac devices such as pacemakers before an MRI scan is essential to allow appropriate safety checks. We assess the accuracy with which a machine learning model can classify the presence or absence of a pacemaker on pre-existing chest radiographs. A total of 7973 chest radiographs were collected, 3996 with pacemakers visible and 3977 without. Images were identified from information available on the radiology information system (RIS) and correlated with report text. Manual review of images by two board certified radiologists was performed to ensure correct labeling. The data set was divided into training, validation, and a hold-back test set. The data were used to retrain a pre-trained image classification neural network. Final model performance was assessed on the test set. Accuracy of 99.67% on the test set was achieved. Re-testing the final model on the full training and validation data revealed a few additional misclassified examples which are further analyzed. Neural network image classification could be used to screen for the presence of cardiac devices, in addition to current safety processes, providing notification of device presence in advance of safety questionnaires. Computational power to run the model is low. Further work on misclassified examples could improve accuracy on edge cases. The focus of many healthcare applications of computer vision techniques has been for diagnosis and guiding management. This work illustrates an application of computer vision image classification to enhance current processes and improve patient safety.

Chest X-ray Foreign Objects Detection Using Artificial Intelligence

“…In comparison, the model created for our study analyzes as many as 250 X-ray images per second. A study by Thurston et al aimed to create a similar tool, which achieved 99.67% accuracy on the test set [ 39 ]. Also, models developed by White et al to support detection and identification of LLIED on CXRs performed prior to scheduled MRI scans achieved accuracies of 94.5% and 95%, respectively.…”

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions

“…The practical clinical use-case 65 , 66 inspiring our initial development 36 is distinctively different from the most closely corresponding pursuits, 30 – 35 due to its focus on the continuously evolving array of modern much-smaller LLIEDs being inserted into the chest with greater frequency. To our knowledge, this is the first reported achievement of AI-based radiographic detection and identification (important to FDA recalls, such as the Nanostim LLP for dysfunction, as well as to MRI safety) directed at LLIEDs, ranging from MRI-conditional to MRI-unsafe.…”

“…The practical clinical use-case 65,66 inspiring our initial development 36 is distinctively different from the most closely corresponding pursuits, [30][31][32][33][34][35] due to its focus on the continuously evolving array of modern much-smaller LLIEDs being inserted into the chest with greater frequency.…”

“… 32 However, the same AI model demonstrated a lower manufacturer-identification accuracy of 71% compared to another AI model running on either a mobile phone application or web platform 33 (accuracy 89% and 73%, respectively), thereby approximating the nonAI-based CARDIA-X performance (i.e., accuracy 85%). 34 None of the aforementioned studies or a very recently reported study of only lead-dependent pacemaker detection, 35 focused on the recognition of the continuously evolving array of much-smaller modern LLIEDs (which do not display radiographic text-based identifiers).…”