UMBC CSEE professor Mehdi Kiani and his colleagues and students are developing personalized and precise treatments to improve patients' quality of life using AI, wearable tech, and other innovations. They recently teamed up with colleagues at New York Institute of Technology and Pennsylvania State University to develop a system that combines state-of-the-art, millimeter-sized medical implants, computational models, and machine learning to better monitor and treat stomach disorders. 

The NIH-funded "CyberGut" project will use physics-informed neural networks that combine data-driven learning with fundamental physical laws. "When enhanced with human-like learning strategies, such as self-learning and adaptive optimization, these networks can evolve into significantly more powerful analytical tools," Penn State collaborator Farnaz Tehranchi says.

UMBC CSEE professors Konstantinos Kalpakis and Dong Li received research funding from the University of Maryland, Baltimore’s Institute for Clinical and Translational Research to collaborate with UMB partners to develop innovative healthcare technology.

Professor Kalpakis and his colleagues are developing a new approach to help trauma physicians make better-informed treatment decisions quickly. The team is working on a machine-learning framework that can help ER doctors predict medical outcomes, such as which patients will experience severe complications from trauma and what their blood transfusion needs may be.

Professor Li's work will use AI technology to develop a reliable method to monitor blood pressure on a smartphone outfitted with ultrasound sensors to measure blood flow and using the built-in microphone to record heart sounds. The partnership with UMB will allow the team to conduct clinical trials, with both healthy individuals and those with hypertension, to rigorously evaluate the system’s accuracy and effectiveness.