@INPROCEEDINGS{Khal2110:EdgeEcho,
AUTHOR="Aman Khalid and Flavio Esposito and Alessio Sacco and Steven Smart",
TITLE="{EdgeEcho:} An Architecture for Echocardiology at the Edge",
BOOKTITLE="17th International Conference on Network and Service Management (CNSM 2021)",
ADDRESS="Izmir, Turkey",
DAYS=24,
MONTH=oct,
YEAR=2021,
KEYWORDS="Echocardiology; Resource provisioning; Edge computing",
ABSTRACT="Edge computing technologies have improved delays and privacy of several
applications, including in medical imaging and eHealth. In this paper, we
consider ultrasound technology and echocardiology (echo) and empower it
with edge computing.
Despite the many advances that ultrasound technology has seen recently,
e.g., it is possible to perform echo scans using wireless ultrasound
probes, the use of Artificial Intelligence (AI) techniques is becoming a
necessity, for faster and more accurate echo diagnosis (not limited to
heart diseases). While a few proprietary solutions exist that embed AI
within echo devices, none of them uses resource-intensive tasks on handheld
devices, and none of them is open-source. To this end, we propose EdgeEcho,
an architecture that captures ultrasound data originated from handheld
ultrasound probes and tags it using semantic segmentation performed on edge
cloud. Our prototype focuses on optimizing the management of edge resources
to address the specific requirements of echocardiology and the challenges
of serving AI algorithms responsively. As a use case, we focus on a
ventricular volume detection operation. Our performance evaluation results
show that EdgeEcho can support multiple parallel medical video processing
streaming sessions for continuing medical education, demonstrating a
promising edge computing application with life-saving potential."
}