Latest AI and machine learning research in pregnancy for healthcare professionals.
PURPOSE: Recent advances in multimodal large language models (LLMs) have demonstrated promising potential for medical image analysis, yet their diagnostic capability in thyroid ultrasound remains unverified. This study explored the feasibility of ChatGPT-5, the latest multimodal LLM, for thyroid nodule classification and contextualized its diagnostic performance against S-Detect, an FDA-approved c...
Developmental dysplasia of the hip (DDH) causes preventable morbidity when diagnosis is delayed. We review advances that address screening gaps: 3-dimensional (3D) ultrasound for volumetric visualization with retrospective plane selection; artificial intelligence (AI)-assisted 2-dimensional (2D) cine sweeps that add automated quality control and classification for lightly trained operators; and op...
Proteins and peptides from functional foods offer notable health benefits, yet their efficacy is hindered by low solubility, enzymatic degradation, po...
In recent years, convolutional neural network (CNN)-based optical flow models for motion estimation have been applied to radio-frequency (RF) ultrasou...
Three-dimensional (3D) ultrasound vascular imaging (UVI) is essential for visualizing complex vascular structures. Row-column addressed (RCA) arrays, ...
Conventional skin imaging modalities are often bulky, expensive, and impractical for routine dermatology practice. There is a need for a portable, mul...
Cardiovascular disease is the leading cause of global morbidity and mortality, with coronary artery disease representing the primary driver of prematu...
Accurate assessment of liver fibrosis in the left liver lobe remains clinically challenging due to motion artifacts that compromise the reliability of...
OBJECTIVES: Follistatin-like protein-1 (FSTL-1) is emerging as a myokine linking skeletal and muscle biology. We investigated the relationship between...
Precise drug delivery in the biliary tract remains challenging due to the dynamic physiological environment and lack of control in existing systems. H...
Early detection of fetal cardiac diseases can dramatically improve neonatal outcomes by enabling timely intervention and informed clinical management....
This commentary delineates the developmental pathway of artificial intelligence (AI) in ultrasound follicular monitoring, highlighting a paradigm shif...
In medical imaging, segmentation is a critical task for analysis and diagnosis. Deep learning-based segmentation has been actively studied and has sho...
Liposomes are used as a vehicle in targeted drug delivery due to inherent biocompatibility and encapsulative potential for diverse bioactive agents. H...
Artificial intelligence (AI) offers solutions to overcome limitations of fetal MRI, including motion, low signal-to-noise ratio, and slice misregistra...
Cancer remains one of the most challenging diseases to conquer due to its high mortality rate and the lack of effective diagnostic and therapeutic too...
Artificial intelligence is transforming obstetric practice through applications in diagnostic imaging, risk prediction, and clinical decision-making. ...
Congenital heart disease (CHD) is the most common major birth anomaly and a key cause of neonatal mortality. While early diagnosis improves outcomes, ...
OBJECTIVE: This study determines whether a machine-learning model integrating sonographic biometry with maternal clinical parameters improves predicti...
Lipid nanoparticles (LNPs) have demonstrated great potential in drug delivery. To fully unlock the therapeutic effect for various diseases, specific d...