AI-HEAT Research Objectives

The AI-HEAT project is a groundbreaking endeavor aimed at developing a deep learning-powered online platform to accurately distinguish between pediatric febrile conditions, including Kawasaki Disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), Typhus and non-specific febrile illness. These conditions, while sharing several clinical presentations, require significantly different treatments. Early diagnosis and appropriate treatment are crucial for the best possible outcomes for patients with these diseases.

The AI-HEAT system utilizes state-of-the-art deep learning algorithms trained on datasets encompassing demographic, clinical and laboratory information to provide healthcare professionals with accurate and rapid diagnostic support. By leveraging data mining, AI-HEAT seeks to significantly reduce misdiagnosis rates and assist in timely intervention for KD, MIS-C and Typhus. This project represents a crucial step in improving pediatric healthcare, reducing the burden on healthcare systems, and ultimately saving lives. AI-HEAT 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 University of Houston, Texas Children's Hospital and Rady Children's Hospital-San Diego.