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.
KAUST researchers developed VENTIBAG, a mobile AI-powered ventilator, in response to the COVID-19 pandemic. The device extracts and delivers pure oxygen, adjusting support based on real-time monitoring of the patient's condition via cloud connectivity. Funded by a KAUST Innovation Challenge grant, the portable ventilator is now advancing to the testing stage for medical applications. Why it matters: This innovation addresses critical needs for remote patient care and reducing hospital overcrowding, particularly relevant in resource-constrained environments.
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.
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.
Pascal Fua from EPFL gave a talk at MBZUAI on physics-based deep learning for medical imaging. The talk covered how self-supervision and knowledge of human anatomy and physics can improve deep learning algorithms when training data is limited. Applications discussed included endoscopic heart surgery, colonoscopy, and intubation. Why it matters: This highlights the growing importance of domain knowledge and self-supervision in overcoming data scarcity challenges for AI in healthcare applications within the region.