Researchers from MBZUAI have developed EchoCoTr, a novel spatiotemporal deep learning method for estimating left ventricular ejection fraction (LVEF) from echocardiograms. EchoCoTr combines CNNs and vision transformers to overcome the limitations of each when applied to medical video data. The method achieves state-of-the-art results on the EchoNet-Dynamic dataset, demonstrating improved accuracy compared to existing approaches, with code available on GitHub.
MBZUAI researchers developed a new deep learning method for rapid and accurate estimation of clinical measurements from echocardiograms. The method focuses on improving the measurement of the left ventricle ejection fraction, a key indicator of heart health. Their deep learning approach improves upon previous methods by better organizing data representation, enhancing performance and transferability. Why it matters: The AI-driven solution can potentially reduce analysis time for cardiologists, improve patient care, and be particularly beneficial in regions with limited healthcare resources.
This paper introduces a self-supervised contrastive learning method for segmenting the left ventricle in echocardiography images when limited labeled data is available. The approach uses contrastive pretraining to improve the performance of UNet and DeepLabV3 segmentation networks. Experiments on the EchoNet-Dynamic dataset show the method achieves a Dice score of 0.9252, outperforming existing approaches, with code available on Github.
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.
This paper introduces an explainable machine learning framework for early-stage chronic kidney disease (CKD) screening, specifically designed for low-resource settings in Bangladesh and South Asia. The framework utilizes a community-based dataset from Bangladesh and evaluates multiple ML classifiers with feature selection techniques. Results show that the ML models achieve high accuracy and sensitivity, outperforming existing screening tools and demonstrating strong generalizability across independent datasets from India, the UAE, and Bangladesh.
KAUST researchers have identified the gene 'CIROZ' as responsible for pediatric heart defects and misplacement of internal organs, working with institutes in Saudi Arabia and worldwide. The research examined samples from 16 patients from 10 families, including four from Saudi Arabia, revealing CIROZ's role in embryonic development symmetry. The findings provide insights into heritable diseases, which are more prevalent in Saudi Arabia. Why it matters: Identifying this gene allows for focused research on preventative strategies and curative therapies for congenital heart defects, particularly relevant in regions with higher rates of such diseases.
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.