MBZUAI and Corniche Hospital researchers have developed FetalCLIP, a foundation model for analyzing fetal ultrasound images to detect congenital conditions. FetalCLIP outperformed other foundation models on ultrasound analysis tasks. The AI model aims to improve the early diagnosis of ailments like congenital heart defects. Why it matters: This innovation has the potential to dramatically improve health outcomes for millions of children annually by providing physicians with better insights into fetal health.
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 researchers led by Dr. Mohammad Yaqub are developing AI algorithms for real-time medical diagnoses, including tools for multiple sclerosis and congenital heart disease. The team developed ScanNav, an AI fetal anomaly assessment system licensed by GE Healthcare for Voluson SWIFT ultrasound machines. ScanNav assists doctors during anomaly scans after 20 weeks of gestation to check for conditions like heart issues and spina bifida. Why it matters: This research has the potential to significantly improve the speed and accuracy of medical diagnoses in the UAE and beyond, addressing critical gaps in healthcare.
MBZUAI researchers are working to improve computer vision models by incorporating common sense knowledge. They aim to address issues like the generation of unrealistic human features, such as hands with incorrect numbers of fingers. By integrating common-sense knowledge, like the fact that humans typically have five fingers per hand, they seek to make deep learning models more reliable. Why it matters: This research could improve the accuracy and trustworthiness of AI-generated content, making it more suitable for real-world applications.
This paper introduces a multi-task learning approach for fetal biometric estimation from ultrasound images, classifying regions (head, abdomen, femur) and estimating parameters. The model, a U-Net architecture with a classification head, achieved a mean absolute error of 1.08 mm for head circumference, 1.44 mm for abdomen circumference, and 1.10 mm for femur length, with 99.91% classification accuracy. The researchers are affiliated with MBZUAI. Why it matters: This research demonstrates advancements in automated fetal health monitoring using AI, potentially improving prenatal care and diagnostics in the region.
KAUST and the King Salman Center for Disability Research (KSCDR) have signed an MoU to collaborate on the diagnosis, management, and treatment of disabilities affecting Saudi citizens and residents. The partnership will focus on neurodevelopmental conditions, learning disabilities, visual impairments, speech disorders, and mobility impairments. KAUST's Center of Excellence for Smart Health, launched on July 1, will be a key component, leveraging its supercomputing resources and genome sequencing capabilities. Why it matters: This partnership aims to address the increasing prevalence of chronic diseases and disabilities in Saudi Arabia, aligning with national research priorities and improving the quality of life for people with disabilities.