ArticleEuropean radiology2021
Deep learning model for predicting gestational age after the first trimester using fetal MRI.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled 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
- MRI-based fetal gestational age estimation using a structure-aware self-supervised network.European radiology · 2026Article
- Deep learning based gestational age estimation from multi-view fetal brain magnetic resonance imaging.Pediatric radiology · 2026Article
- A prior knowledge-guided convolutional neural network model for predicting fetal brain age from MRI during second and third trimesters.BMC pregnancy and childbirth · 2025Article
- [Research Progress in Magnetic Resonance Imaging of Fetal Ventriculomegaly].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Review
- Fetal neuroimaging applications for diagnosis and counseling of brain anomalies: Current practice and future diagnostic strategies.Seminars in fetal & neonatal medicine · 2024Review
- Three-Dimensional Measurement of the Uterus on Magnetic Resonance Images: Development and Performance Analysis of an Automated Deep-Learning Tool.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence applied to fetal MRI: A scoping review of current research.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
- Prediction of body weight from chest radiographs using deep learning with a convolutional neural network.Radiological physics and technology · 2023Article
- Gestational age-related changes in the fetal functional connectome: in utero evidence for the global signal.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Comparison between pystan and numpyro in Bayesian item response theory: evaluation of agreement of estimated latent parameters and sampling performance.PeerJ. Computer science · 2023Article
- Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics.Entropy (Basel, Switzerland) · 2022Article
- Estimated date of delivery with electronic medical records by a hybrid GBDT-GRU model.Scientific reports · 2022Article
- Artificial intelligence in obstetrics.Obstetrics & gynecology science · 2022Article
- Attention-guided deep learning for gestational age prediction using fetal brain MRI.Scientific reports · 2022Article
- Fetal MRI-based artificial intelligence in gestational age prediction--a practical solution to an unsolved problem?European radiology · 2021Article
- Probing an AI regression model for hand bone age determination using gradient-based saliency mapping.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate a deep learning model for predicting gestational age from fetal brain MRI acquired after the first trimester in comparison to biparietal diameter (BPD). MATERIALS AND
methodsOur Institutional Review Board approved this retrospective study, and a total of 184 T2-weighted MRI acquisitions from 184 fetuses (mean gestational age: 29.4 weeks) who underwent MRI between January 2014 and June 2019 were included. The reference standard gestational age was based on the last menstruation and ultrasonography measurements in the first trimester. The deep learning model was trained with T2-weighted images from 126 training cases and 29 validation cases. The remaining 29 cases were used as test data, with fetal age estimated by both the model and BPD measurement. The relationship between the estimated gestational age and the reference standard was evaluated with Lin's concordance correlation coefficient (ρc) and a Bland-Altman plot. The ρc was assessed with McBride's definition.
resultsThe ρc of the model prediction was substantial (ρc = 0.964), but the ρc of the BPD prediction was moderate (ρc = 0.920). Both the model and BPD predictions had greater differences from the reference standard at increasing gestational age. However, the upper limit of the model's prediction (2.45 weeks) was significantly shorter than that of BPD (5.62 weeks).
conclusionsDeep learning can accurately predict gestational age from fetal brain MR acquired after the first trimester. KEY POINTS: • The prediction of gestational age using ultrasound is accurate in the first trimester but becomes inaccurate as gestational age increases. • Deep learning can accurately predict gestational age from fetal brain MRI acquired in the second and third trimester. • Prediction of gestational age by deep learning may have benefits for prenatal care in pregnancies that are underserved during the first trimester.
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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.