Although there have been significant advancements in Left Ventricular Assist Device(LVAD) technology and improvements in mortality rates, infection remains one of the major complications associated with LVAD therapy with an incidence of 25-80% cases annually. Amongst such infections driveline infections are the most common and account for 14-28% according to the RE-MATCH trial of the total of LVAD related infections. If a patient is diagnosed with LVAD infection, it is important to initiate antibiotic therapy as early as possible. If left untreated it can lead to sepsis with multi-organ failure, longer hospital stay, delay heart transplant or early mortality. To improve infection detection and monitoring we propose InDetector, a driveline infection detection system that allows at-home patients to use a smartphone to capture images of their driveline regions to check for infections. InDetector uses a Convolutional Neural Network along with image augmentation techniques for inferring infected images and achieves an overall classification accuracy of 93.75% on our validation dataset.
ASJC Scopus subject areas
- Medicine (miscellaneous)
- Health Informatics
- Computer Science Applications
- Information Systems
- Health Information Management