Researchers at MBZUAI developed a method to measure vital signs using webcams by analyzing color intensity changes in facial blood flow. They built a digital twin system that uses machine learning to combine heart rate, respiratory rate, and blood oxygen level measures. The system displays real-time vital sign information, enabling remote patient triage. Why it matters: This research contributes to the advancement of telemedicine, potentially improving healthcare access in underserved regions and aligning with UN Sustainable Development Goal #3.
MBZUAI is developing AI algorithms to intelligently process data from wearables and home sensors for remote patient monitoring. The algorithms aim to analyze multiple strands of health data to provide a more comprehensive view of a patient's health, distinguishing between genuine emergencies and benign situations. MBZUAI's provost, Professor Fakhri Karray, believes this approach could handle 20-25% of diagnoses virtually, reducing the burden on healthcare systems. Why it matters: This research could significantly improve healthcare efficiency and accessibility in the UAE and beyond by enabling more effective remote patient monitoring and reducing unnecessary hospital visits.
Pong C Yuen from Hong Kong Baptist University will present a talk on remote photoplethysmography (rPPG) detection. The talk will review the development of rPPG detection, share recent research, and discuss future directions. rPPG is a technology for non-contact computer vision and healthcare applications like heart rate estimation. Why it matters: Advancements in rPPG could enable new remote patient monitoring and diagnostic tools in the region, reducing the need for physical contact.
Video motion magnification amplifies subtle movements in video footage, making the imperceptible visible across various fields. In healthcare, it allows non-invasive monitoring of vital signs and micro-expressions. In engineering, it helps detect structural vibrations in infrastructure, while also being used in sports science, security, and robotics. Why it matters: The technology's ability to reveal hidden details has the potential to revolutionize diagnostics, monitoring, and decision-making in diverse sectors across the Middle East.
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.
A retrospective study in Abu Dhabi, UAE, developed a machine learning-based prognostic system to predict the risk of seven complications in COVID-19 patients using data from 3,352 patient encounters. The system, trained on data from the first 24 hours of admission, achieved high accuracy (AUROC > 0.80) in predicting complications like AKI, ARDS, and elevated biomarkers in geographically split test sets. The models primarily used gradient boosting and logistic regression.
KAUST researchers collaborated to identify molecular pathways for plant biofortification of vitamin A. A KAUST group demonstrated high pressure conversion of carbon dioxide into useful products. Another team designed a biosensor using metal oxide transistors to detect glucose in saliva. Why it matters: These projects highlight KAUST's contributions to biotechnology, environmental sustainability, and healthcare through advanced materials and molecular techniques.
A KAUST team led by Xin Gao developed an AI model for COVID-19 detection from CT scans, addressing limitations of existing methods. The model incorporates a novel embedding strategy, a CT scan simulator, and a 2.5D deep-learning algorithm. Tested at King Faisal Specialist Hospital, the model demonstrated high accuracy in detecting COVID-19 cases. Why it matters: This research provides a valuable tool for rapid and accurate COVID-19 diagnosis in the region, especially in early-stage infections, improving healthcare outcomes.