AI-MET Research Objectives

The AI-MET project is a groundbreaking endeavor that focuses on developing a Deep Learning-based method in the form of an online platform to accurately differentiate between Multisystem Inflammatory Syndrome in Children (MIS-C) and Endemic Typhus, two diseases with similar clinical presentations but vastly different treatment approaches. MIS-C is a rare but severe condition primarily affecting children, often post-COVID-19, while Endemic Typhus is a bacterial infection transmitted by fleas or mites. These similarities in symptomatology often lead to misdiagnoses and delayed treatment, posing a serious threat to patient outcomes.

The AI-MET system aims to employ state-of-the-art deep learning algorithms trained on datasets encompassing clinical, laboratory, and radiological information to provide healthcare professionals with accurate and rapid diagnostic support. By leveraging data analysis, AI-MET aims to significantly reduce misdiagnosis rates and assist in timely intervention for both MIS-C and Endemic Typhus. This project represents a crucial step in improving pediatric healthcare, ultimately saving lives and reducing the burden on healthcare systems. AI-MET strives to exemplify the potential of artificial intelligence in addressing complex clinical challenges and ensuring better outcomes for affected children.

Partners and Collaborators

We are an interdisciplinary team composed of members from the University of Houston and the Texas Children's Hospital.