Collaborative Study: More Than Skin Deep

Law enforcement and legal professionals often rely on the accuracy and interpretation of injury documentation to inform their decisions; however, current bruise assessments provide little reliable data.

Researchers in Mason’s College of Public Health and the College of Engineering and Computing received a $988,559 grant from the U.S. Department of Justice, Office of Justice Programs, for a three-year study pairing forensic bruise analysis with machine learning.

“Image analysis using deep learning, which is a subdomain of machine learning, demonstrates significant benefits in accuracy and reliability within health care, yet few studies have applied these techniques to the forensic analysis of injuries,” says Associate Professor in the College of Public Health, Katherine Scafide, a forensic nurse and the principal investigator.

The study seeks to use deep learning to help improve our understanding of how bruises appear over time on diverse skin tones. The results will broadly affect forensic clinical practice, criminal justice response, and future research.

Associate Professor David Lattanzi brings to the research team his expertise in using deep machine learning and image analysis to track damage to civil infrastructure. “It’s exciting to take what we’ve learned from analyzing damage to infrastructure and apply it in a way that deepens the societal impact of our work,” says Lattanzi.

“This project would not exist without Dave,” says Scafide. He brings a wealth of knowledge and expertise in artificial intelligence when it comes to analyzing images. It’s fairly groundbreaking. It’s a massive component of this particular project, and he is leading that effort.”

The team will develop a new, quantitative approach to identify the age of a bruise using deep learning models while determining its reliability and accuracy. Additionally, the team will develop a secure, searchable platform to store digital bruise images providing a comprehensive look at the healing process across diverse skin tones. The platform will integrate deep learning modeling and support future research and collaboration within the forensic science community.

“There is great potential for machine learning to support and improve forensic nursing techniques and reporting. Machine learning helps solve complex problems, and in forensic nursing, it can aid in the understanding of bruise tones over time,” says Director of the Machine Learning and Inference Laboratory Janusz Wojtusiak

“This new grant from the Justice Department exemplifies how we were able to turn a multidisciplinary and collaborative research-learning experience for undergraduates into a federally-funded and impactful research project that can benefit the health and wellbeing of vulnerable populations,” says Scafide. g

Associate Professor Girum Urgessa began studying what happens when things explode as a graduate student at the University of New Mexico. He says his line of research is one of the coolest things around and jokes that it was great fun to take things out to the desert and blow them up. Of course, exploding buildings and bridges in real time is costly and disruptive to the environment, so mathematical modeling is a valuable substitute.

Since coming to Mason, Urgessa has moved from studying air blasts to examining underwater explosions. He is now part of an interdisciplinary team looking at these explosions and their effects on civil engineering infrastructure (specifically bridge piers) with the support of a $1.5 million grant from the Defense Threat Reduction Agency.

“There is a demand for experts capable of conducting blast-structure interactions and structural integrity assessments. At Mason, we continue to mentor exceptional post-doctoral research scholars and graduate students needed to address these complex physics and engineering problems,” he says.

Lingquan Li, a postdoctoral research fellow, works alongside Urgessa, Rainald Löhner of the College of Science, PhD student Facundo Airaudo, and master’s student Jacob Sanders are also on the team. Urgessa and Löhner are the co-principal investigators on the project.