Daniel Spalinski

School: University of Notre Dame

Major: Physics

DOI: https://doi.org/10.21985/n2-w7jz-tn30

My name is Daniel Spalinski, and I am a rising senior Physics in Medicine major and Energy Studies minor at the University of Notre Dame. I have lived my whole life in Harwood Heights, IL, a northwest suburb just outside of Chicago. My research project from my summer at UCI in Irvine, California applies image detection methods to investigate and quantify cellular biomechanical properties. My graduate mentor Cody Combs supported me every step of the way and was a pleasure to work with and learn from. At Notre Dame, I am currently part of the biomedical photonics group and work with diffuse correlation spectroscopy and its applications for blood flow measurements. My other interests include medical physics, AI in medical imaging, and mathematical modeling of biological systems. I hope to matriculate into an MD or MD/PhD program after graduation and have a career that is centered around applying my background to improve clinical care and translational research. Outside of my academic work, you can catch me playing tennis, basketball, guitar, or following the NBA and my hometown Chicago Bulls. 

Applying Machine Learning To Analyze Cell Deformation In Microchannels

Abstract

This project seeks to investigate whether a machine learning algorithm is capable of differentiating cancer cells by their biomechanical properties as well as identify the type of microscopy best suited for this task. Microfluidics channels have been used to detect and analyze cell deformation that is highly dependent on cell type and health. HL-60 leukemia cell stiffness and biomechanics are known to be correlated to the stage and progression of the cancer. Microfluidics channels and visual machine learning analysis software are developed in order to detect quantify deformation in these cells. The cells are passed through the channels and recorded with a high-speed camera using bright field and phase microscopy. Distant separation in cell compression demonstrates the viability of phase microscopy using the current software to detect cells based on deformability. Phase microscopy shows the potential to provide data for three-dimensional deformation given proper software adjustment.