ArticleThe British journal of radiology2023
Artificial intelligence applied to fetal MRI: A scoping review of current research.
Article in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of artificial intelligence in early childhood health management: a systematic review from fetal to pediatric periods.Frontiers in pediatrics · 2025Pooled it
- Article
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Review
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- Fetal Assessment Suite (FetAS): a web-based platform for automatic fetal MRI analysis using AI.Scientific reports · 2025Article
- Enhanced Brain Stroke Lesion Segmentation in MRI Using a 2.5D Transformer Backbone U-Net Model.Brain sciences · 2025Article
- Review
- Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges.The British journal of radiology · 2024Article
- A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery.International journal of women's health · 2024Review
- Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image.Scientific reports · 2023Article
- What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals.BMJ open · 2023Article
- Fetal MRI: what's new? A short review.European radiology experimental · 2023Review
- Prenatal imaging advances: physiology and function to motion correction and AI-introductory editorial.The British journal of radiology · 2023Article
- Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging.World journal of clinical cases · 2023Review
- Machine learning and disease prediction in obstetrics.Current research in physiology · 2023Review
- Fetal and Maternal Diseases in Pregnancy: From Morphology to Function.Diagnostics (Basel, Switzerland) · 2022Article
- PANDA: Patch-based unsupervised deep learning for brain anomaly detection via age prediction in fetal MRI.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to 'learn' and 'adapt' without explicit instructions meaning that computer systems can 'evolve' and hopefully improve without necessarily requiring external human influences. The potential for this novel technology has resulted in great interest from the medical community regarding how it can be applied in healthcare. Within radiology, the focus has mostly been for applications in oncological imaging, although new roles in other subspecialty fields are slowly emerging.In this scoping review, we performed a literature search of the current state-of-the-art and emerging trends for the use of artificial intelligence as applied to fetal magnetic resonance imaging (MRI). Our search yielded several publications covering AI tools for anatomical organ segmentation, improved imaging sequences and aiding in diagnostic applications such as automated biometric fetal measurements and the detection of congenital and acquired abnormalities. We highlight our own perceived gaps in this literature and suggest future avenues for further research. It is our hope that the information presented highlights the varied ways and potential that novel digital technology could make an impact to future clinical practice with regards to fetal MRI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.