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.
Researchers from KAUST, King Abdulaziz University, and King Abdulaziz University Hospital conducted a study comparing stem cells from Saudi Klinefelter patients with those from North American and European descent. Klinefelter syndrome affects approximately one in 600 Saudi males, but the MENA population is underrepresented in genomic studies of the disease. The study found a subset of genes on the X chromosome whose dysregulation characterizes Klinefelter syndrome, regardless of geographic origin or ethnicity. Why it matters: This research addresses a gap in understanding the molecular basis of Klinefelter syndrome in the MENA population and provides a platform for further studies of chromosomal diseases.
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.
Researchers at MBZUAI introduce FissionFusion, a hierarchical model merging approach to improve medical image analysis performance. The method uses local and global aggregation of models based on hyperparameter configurations, along with a cyclical learning rate scheduler for efficient model generation. Experiments show FissionFusion outperforms standard model souping by approximately 6% on HAM10000 and CheXpert datasets and improves OOD performance.
Researchers propose a universal anatomical embedding (UAE) framework for medical image analysis to learn appearance, semantic, and cross-modality anatomical embeddings. UAE incorporates semantic embedding learning with prototypical contrastive loss, a fixed-point-based matching strategy, and an iterative approach for cross-modality embedding learning. The framework was evaluated on landmark detection, lesion tracking and CT-MRI registration tasks, outperforming existing state-of-the-art methods.