Researchers at Khalifa University have developed an AI system capable of predicting cardiovascular disease (CVD) risks up to 12 years in advance. The AI model uses data from the Framingham Heart Study to assess long-term CVD risk factors. It outperforms existing methods in predicting CVD incidence over extended periods. Why it matters: This advancement could significantly improve preventative healthcare strategies in the UAE and globally by enabling earlier interventions for individuals at high risk of heart disease.
MBZUAI and Abu Dhabi Health Services Company (SEHA) are collaborating to develop AI algorithms to predict heart attacks months in advance with 87% accuracy using ultrasound images. The project aims to preemptively predict heart attacks in the short and long term, addressing the high rates of cardiac arrest, especially in the Middle East. A Memorandum of Understanding (MoU) was signed between SEHA and MBZUAI to integrate AI into healthcare. Why it matters: This partnership could significantly improve healthcare outcomes in the region by leveraging AI to proactively address heart disease, a leading cause of death.
MBZUAI alumnus Ikboljon Sobirov is using AI to develop new diagnostic tools for cardiovascular disease at the University of Oxford. His research focuses on building imaging biomarkers by integrating transcriptomic data with medical scans. The goal is to predict how a patient will respond to specific medications using only images. Why it matters: This work showcases the potential of AI and multi-modal data to personalize medicine and improve healthcare outcomes in the region and globally.
Researchers from KAUST, King Faisal Specialist Hospital, and collaborators have developed a new method to predict cardiometabolic disease risk in underrepresented ethnic populations using genetic information and public databases. The study focused on Arab communities and created a framework to determine polygenic scores for more accurate heart disease prediction. The framework was validated using records of over 5,000 Arab patients, demonstrating that genetic risk complements conventional risk factors. Why it matters: This research addresses a critical gap in genomic data for non-European populations, potentially leading to more effective and personalized healthcare strategies in the Arab world and beyond.
MBZUAI researchers led by Dr. Mohammad Yaqub are developing AI algorithms for real-time medical diagnoses, including tools for multiple sclerosis and congenital heart disease. The team developed ScanNav, an AI fetal anomaly assessment system licensed by GE Healthcare for Voluson SWIFT ultrasound machines. ScanNav assists doctors during anomaly scans after 20 weeks of gestation to check for conditions like heart issues and spina bifida. Why it matters: This research has the potential to significantly improve the speed and accuracy of medical diagnoses in the UAE and beyond, addressing critical gaps in healthcare.