MBZUAI and Sheikh Shakbout Medical City researchers developed PECon, a deep learning method for pulmonary embolism detection using CT scans and electronic health records. PECon uses neural networks and contrastive learning to encode and align image and text data. The method aims to improve diagnosis accuracy and speed, potentially saving lives. Why it matters: This research demonstrates AI's potential to enhance medical diagnostics in the UAE, addressing a critical healthcare challenge.
This paper introduces Pulmonary Embolism Detection using Contrastive Learning (PECon), a supervised contrastive pretraining strategy using both CT scans and EHR data to improve feature alignment between modalities for better PE diagnosis. PECon pulls sample features of the same class together while pushing away features of other classes. The approach achieves state-of-the-art results on the RadFusion dataset, with an F1-score of 0.913 and AUROC of 0.943.
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's Dr. Mohammad Yaqub is developing AI algorithms to power point-of-care ultrasound (PoCUS) on mobile devices, expanding on his prior work on an AI-based fetal anomaly system used in GE Healthcare's ultrasound. These algorithms aim to make smaller, affordable PoCUS devices accessible in remote areas for faster diagnoses. The handheld devices, costing around $5000 USD, can connect to mobile devices and provide intelligence to interpret images, addressing the shortage of specialists in remote locations. Why it matters: This initiative democratizes access to critical diagnostic tools, potentially saving lives by enabling early detection of life-threatening conditions in underserved communities.
Researchers at KAUST have developed a nanocomposite material that converts X-rays into light with nearly 100% efficiency. The material combines a metal-organic framework (MOF) containing zirconium with an organic TADF chromophore. This design achieves high resolution and sensitivity in X-ray imaging, potentially reducing medical imaging doses by a factor of 22. Why it matters: This innovation could lead to more efficient and safer medical imaging and security screening technologies in the region and beyond.
Researchers at ETH Zurich have formalized models of the EMV payment protocol using the Tamarin model checker. They discovered flaws allowing attackers to bypass PIN requirements for high-value purchases on EMV cards like Mastercard and Visa. The team also collaborated with an EMV consortium member to verify the improved EMV Kernel C-8 protocol. Why it matters: This research highlights the importance of formal methods in identifying critical vulnerabilities in widely used payment systems, potentially impacting financial security for consumers in the GCC region and worldwide.
MBZUAI graduate Ahmed Sharshar developed a computer vision application that assesses lung health from a video of a person breathing, estimating Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), and Peak Expiratory Flow (PEF). The model achieved up to 100% accuracy using thermal video data from 60 participants. Sharshar aims to create lightweight models applicable in developing countries without high-end GPUs. Why it matters: This research showcases the potential of AI to democratize healthcare access through non-invasive, accessible diagnostic tools.
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.