Analysis of Potential for User Errors in Mobile Deployment of Radiology Deep Learning for Cardiac Rhythm Device Detection.

ABSTRACT: We examine how convolutional neural networks (CNNs) for cardiac rhythm device detection can exhibit failures in performance under suboptimal deployment scenarios and examine how medically adversarial image presentation can further impair neural network performance. We validated the publicly available Pacemaker-ID web server and mobile app on 43 local hospital emergency department (ED) cases of patients presenting with a cardiac rhythm device on anterior-posterior (AP) chest radiograph and assessed performance using Cohen's kappa coefficient for inter-rater reliability. To illustrate adversarial performance concerns, we then produced example CNN models using the 65,379 patient MIMIC-CXR chest radiograph retrospective database and evaluated performance with area under the receiver operating

SUBMITTER: Sabottke C 

PROVIDER: S-EPMC8329116 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature