ArticleRadiology. Artificial intelligence2025
Deep Learning-based Brain Age Prediction Using MRI to Identify Fetuses with Cerebral Ventriculomegaly.
Article in Radiology. Artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Developmental Brain Age Estimation From MRI Data: A Systematic Review of Deep Learning Approaches and Open Datasets.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- AI-Driven Clinical Decision Support System for Automated Ventriculomegaly Classification from Fetal Brain MRI.Journal of imaging · 2025Article
- Conditional deep generative normative modeling for structural and developmental anomaly detection in the fetal brain.NeuroImage · 2025Article
- PANDA: Patch-based unsupervised deep learning for brain anomaly detection via age prediction in fetal MRI.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impaired neurodevelopmental outcomes. Recently, a novel fetal brain age prediction method using a two-dimensional (2D) single-channel convolutional neural network (CNN) with multiplanar MRI sections showed the potential to detect fetuses with VM. This study examines the diagnostic performance of a deep learning-based fetal brain age prediction model to distinguish fetuses with VM (
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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.