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.
The KAUST Smart-Health Initiative (KSHI) held its annual forum, showcasing research collaborations with partners like KFSHRC, KAIMRC, and KACST. Projects presented included biomarker detection devices, cardiovascular disease sensors, 3D data visualization, and genome sequencing for patient data analysis. Dr. Sara F. Althari highlighted KAUST's cultivation of partnerships within the Kingdom's healthcare and biotech ecosystem. Why it matters: The KSHI aims to transform Saudi Arabia's healthcare system towards precision and personalized medicine, aligning with Vision 2030.
KAUST Ph.D. student Mohamed Bahloul received a best paper award at the IEEE Engineering in Medicine and Biology Society (EMBC ‘18) for the Africa and Middle East region. Bahloul's paper presented a three-element fractional-order viscoelastic Windkessel model developed in the EMAN group at KAUST. The model incorporates a fractional-order capacitor, potentially enabling earlier prediction of cardiovascular diseases. Why it matters: The award recognizes impactful research in biomedical engineering at KAUST and highlights the potential for advanced modeling techniques to improve healthcare in the region.
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.
MBZUAI researchers are developing AI applications for malaria prevention in Indonesia using sensory data fusion and digital twins. Another MBZUAI team is using machine learning and computer vision to detect cardiovascular disease from CT scans in collaboration with the University of Oxford. AI-powered remote patient monitoring is also being explored for proactive interventions and chronic disease management. Why it matters: These projects demonstrate the potential of AI to address healthcare challenges in underserved communities and improve disease prevention and management in the region.
MBZUAI celebrated the graduation of 41 leaders from UAE government and business in its inaugural Executive Program. The program, launched in September 2021, included 12 weeks of coursework and collaborative projects. Participants developed AI solutions for employee assessment, net-zero goals, and early detection of cardiovascular disease. Why it matters: The program aims to cultivate AI leadership within the UAE, addressing national challenges and fostering economic transformation.