ABSTRACT
BACKGROUND: Pediatric heart failure is associated with high mortality rates and is a current clinical burden. There is only one FDA approved pediatric VAD, Berlin Heart EXCOR, for treatment. Thrombo-embolic complications are a significant clinical challenge, which can lead to devastating complications such as stroke and impair efficient EXCOR function. Currently, clinicians perform largely qualitative periodic assessment of EXCOR operation by observing the motion of a rapidly moving membrane, which can be prone to human error and can lead to missing out on crucial information. METHODS: In this study, we design and implement a quantitative early warning system for accurate and quantitative assessment of the EXCOR membrane, named EXCOR Membrane Motion Analyzer (EMMA). Using a combination of image analysis, computer vision and custom designed algorithm, we perform rigorous frame by frame analysis of EXCOR membrane video data. We developed specialized metrics to identify relative smoothness between successive peaks, time between peaks and overall smoothness indicators to quantify and compare between multiple cases. RESULTS: Our results demonstrate that EMMA can successfully identify the motion and wrinkles on each video frame and quantify the smoothness and identify the phases of each cardiac cycle. Moreover, EMMA can obtain the smoothness of each frame and the temporal evolution of membrane smoothness across all image frames for the video sequence. CONCLUSIONS: EMMA allows for a fast, accurate, quantitative assessment to be completed and reduces user error. This enables EMMA to be used effectively as an early warning system to rapidly identify device abnormalities.
