Artificial Intelligence in Fetal MRI: Principles, Applications, Limitations, and Future Directions

Artificial intelligence (AI) offers solutions to overcome limitations of fetal MRI, including motion, low signal-to-noise ratio, and slice misregistration. This review summarizes current AI applications in fetal MRI, focusing on image enhancement, automated segmentation, quantitative analysis, and emerging multimodal approaches. AI improves reconstruction, denoising, motion correction, and volumetric assessment, and supports tasks such as gestational-age estimation and anomaly detection. However, most studies rely on small, single-center data sets with limited external validation. Robust multicenter data, standardized protocols, and transparent evaluation frameworks are required before AI can be reliably integrated into routine prenatal imaging.

Authors

Institutions

Publication Details

Journal
Clinical Obstetrics & Gynecology
Published
2025-12-24
DOI
https://doi.org/10.1097/grf.0000000000000987
Citations
1
Primary Topic
Fetal and Pediatric Neurological Disorders
Type
article
Field-Weighted Citation Impact
3.49
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence in Fetal MRI: Principles, Applications, Limitations, and Future Directions

L. Bussieres, D. Grevent, R. Corroënne, Laurent J Salomon
1 citations
Clinical Obstetrics & Gynecology
Fetal and Pediatric Neurological Disorders
3.49
article

Artificial Intelligence in Fetal MRI: Principles, Applications, Limitations, and Future Directions

L. Bussieres, D. Grevent, R. Corroënne, Laurent J Salomon
article en
1 citations

Abstract

Artificial intelligence (AI) offers solutions to overcome limitations of fetal MRI, including motion, low signal-to-noise ratio, and slice misregistration. This review summarizes current AI applications in fetal MRI, focusing on image enhancement, automated segmentation, quantitative analysis, and emerging multimodal approaches. AI improves reconstruction, denoising, motion correction, and volumetric assessment, and supports tasks such as gestational-age estimation and anomaly detection. However, most studies rely on small, single-center data sets with limited external validation. Robust multicenter data, standardized protocols, and transparent evaluation frameworks are required before AI can be reliably integrated into routine prenatal imaging.

Clinical Obstetrics & GynecologyVol. 69(1)
Hôpital Necker-Enfants Malades (FR), Institut Necker Enfants Malades (FR), Université Paris Lumières (FR)
Openalex Percentile: Top 6%
Fetal and Pediatric Neurological Disorders
3.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.