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.
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.
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 developed FetalCLIP, an AI model trained on 210,000 ultrasound images for fast and reliable interpretation of fetal scans. MBZUAI's President Eric Xing contributed to the General Expression Transformer (GET), an AI foundation model acting as a biological simulator to predict gene behavior. MBZUAI and Carleton University created MedPromptX for quicker disease diagnosis and treatment plans using multimodal AI. Why it matters: These AI advancements from MBZUAI have the potential to revolutionize healthcare in the region and globally, from prenatal care to drug discovery and personalized medicine.
MBZUAI's Dr. Mohammad Yaqub is developing AI algorithms to power point-of-care ultrasound (PoCUS) on mobile devices, expanding on his prior work on an AI-based fetal anomaly system used in GE Healthcare's ultrasound. These algorithms aim to make smaller, affordable PoCUS devices accessible in remote areas for faster diagnoses. The handheld devices, costing around $5000 USD, can connect to mobile devices and provide intelligence to interpret images, addressing the shortage of specialists in remote locations. Why it matters: This initiative democratizes access to critical diagnostic tools, potentially saving lives by enabling early detection of life-threatening conditions in underserved communities.