Echocardiographic Epicardial Adipose Tissue Quantification: Challenges and Insights

e-Health applications, as a cornerstone of modern distributed systems, must synergize with advanced analysis methodologies, incorporating image processing, statistical, and predictive techniques to expedite diagnosis and optimize therapeutic strategies. Cardiovascular disease (CVD) presents a formidable health challenge, claiming 18 million lives annually, with projections set to worsen due to population aging, the rise of metabolic diseases, and gaps in effective prevention and precise risk stratification. A pivotal indicator of cardiovascular health, the epicardial adipose tissue (EAT) thickness, is traditionally estimated by medical professionals without a standardized and precise procedure. This paper chronicles our endeavor to automate the delineation of EAT from echocardiogram videos, a fundamental precursor to its thickness quantification. We confronted the intricate task of interpreting echocardiographic data and trialed a variety of image processing methods aimed at clarifying the EAT's representation amidst the heart's dynamic activity and inherent imaging noise. Our study's narrative contributes to the pervasive computing domain, envisaging the deployment of such medical applications as on-demand cloud services for medical experts and institutions, thus fostering collaborative, efficient, and accurate cardiovascular health realtime assessment. Unfortunately, our study failed and in this paper we analyse the reasons and we report the lesson learned.