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 are investigating the sources and chemistry of airborne particles to tackle urban air pollution. The research integrates laboratory simulations of atmospheric reactions with field measurements to understand the formation mechanisms of particulate matter (PM). They are also developing cellular and animal models to test how different air pollutants affect human health, in collaboration with the Center of Excellence for Smart Health. Why it matters: This research can inform targeted control strategies to manage emissions and improve air quality in Saudi Arabia and other countries facing similar pollution challenges.
KAUST is hosting Junfeng (Jim) Zhang from Duke University to study air pollution's impact on health in Saudi Arabia. Zhang will collaborate with KAUST faculty to assess the health effects of environmental stressors using epidemiology and toxicology. Air pollution causes significant premature deaths and loss of life expectancy in Saudi Arabia. Why it matters: This research will inform evidence-based policies and treatment strategies to combat respiratory illnesses linked to air pollution in Saudi Arabia and the broader region.
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.
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.
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 and Saudi Aramco collaborated to develop a laser-based sensor for detecting trace amounts of gas leaks in petrochemical plants. The sensor uses machine learning to identify specific gases, differentiating it from previous sensors that only detect large leaks. The technology can differentiate between closely related industrial gases like benzene, toluene, ethyl benzene and xylene (BTEX). Why it matters: This innovation enables proactive monitoring and rapid pinpointing of leaks, enhancing safety, environmental protection, and operational efficiency in the petrochemical industry.
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.