BME Masters Project: Samantha Lopez- “Cell Aggregate Calcification Model Classification with Machine Learning”

Master’s Project Defense

Tuesday, April 23, 2024

Gateway Park, Room GP 1002

1:00pm — 1:45pm

“Cell Aggregate Calcification Model Classification with Machine Learning”

Samantha Lopez

Abstract: Calcific aortic valve disease (CAVD) is a significant source of mortality in which calcium deposits stiffen on the valve leaflets which decreases flow of oxygenated blood flow to the body. To study the mechanisms of calcification, in vitro models involving aggregated cells from a monolayer are utilized by researchers. In our previous work, we created 2D microcontact-printed cellular aggregates on collagen/gel substrates to form rapid and uniform aggregates to study the formation of calcific nodules. Although more uniform than self-aggregated models, substantial variability is still observed with aggregates with variable levels of confluence which is hindering progress in determining mechanisms of disease and for evaluating potential treatments. The goal of this work is to develop machine learning algorithms for rapid and objective aggregate classification based on visual cell density and “class” of aggregate to decrease variability of aggregates used for the study of calcification in vitro. Through image preprocessing and machine learning classification, aggregate classification is up to 75% accurate. The results demonstrate promising outcomes for aggregate classification using machine learning techniques, therefore minimizing variability in future experimental studies.

For a zoom link, please email kharrison@wpi.edu or jnorton@wpi.edu