Aug 2026· Developmental Medicine & Child Neurology· 0 citations· 10 references
Medicine
TL;DR
The major barriers to widespread clinical adoption of artificial intelligence tools are examined and future directions at the clinical interface are outlined that may further transform the diagnostic role of fetal MRI.
Abstract
While slice-to-volume registration and super-resolution reconstruction laid the foundation for motion-corrected 3D T2-weighted fetal brain magnetic resonance imaging (MRI) more than two decades ago, advances in deep learning are now enabling automation across acquisition planning, segmentation, biometry, and image quality control. In this narrative review, we highlight these emerging techniques and analyse their strengths and limitations in the context of clinical translation. We examine the major barriers to widespread clinical adoption of artificial intelligence tools and outline future directions at the clinical interface that may further transform the diagnostic role of fetal MRI. Together, these developments underscore a shifting landscape towards more comprehensive, quantitative in-utero assessment, with the potential to enhance diagnostic accuracy and workflow efficiency, and broaden the clinical applications of fetal MRI in prenatal care.
The pivotal role of deep learning in image reconstruction and acceleration is explored, followed by detailed analyses of quantitative brain oxygen metabolism assessment, standardized spinal cord imaging frameworks, and the non-invasive monitoring of the glymphatic system using diffusion MRI.
Xun-Yang Zhang, A. Hagiwara, Masaya Takahasi et al.· Japanese Journal of Radiolog...· 0 citations
Clinical brain MRIs from pediatric health systems represent a viable resource for modeling early neurodevelopmental trajectories and studying neurodevelopmental risk in real-world populations, and a robust segmentation approach tailored to early-life clinical MRIs with variable orientation, resolution, and contrast is...
E. Levitis, Henry F. J. Tregidgo, D. Zimmerman et al.· medRxiv· 0 citations
Objective: Accurate volumetric analysis of the brain and cerebrospinal fluid (CSF) is essential for monitoring hydrocephalus, a significant pediatric neurological condition. While computed tomography (CT) provides high-quality volumetric assessment, its associated ionizing radiation poses risks, especially for children...
S. Mukherjee, K. Templeton, S. J. Schiff et al.· medRxiv· 0 citations
A 3D nnU-Net model for CP segmentation based on manually labeled DIR masks achieves highly accurate CP segmentation that generalizes across scanners and captures the inferior extent of the C-shaped structure often missed by conventional models.
M. Lauerer, Julian McGinnis, Cornelius Berberich et al.· medRxiv· 0 citations
Three-dimensional fetal brain ultrasound offers non-ionizing, cost-effective imaging with rich spatial information for comprehensive anatomical assessment, yet its development remains limited by scarce data and annotations. In contrast, fetal brain magnetic resonance imaging has advanced further, supported by larger da...
Yuhuan Lu, Sergio Valencia, Yuanji Zhang et al.· 0 citations
An anatomy-aware deep learning framework for enhancement of neonatal 64mT T2-weighted MRI improves anatomical visibility while preserving native ultra-low-field contrast and enabling quantitative structural analysis support the feasibility of quantitative neonatal neuroimaging at ultra-low field.
P. Cawley, A. Uus, K. Colford et al.· medRxiv· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
