ArticleFrontiers in neurology2020
Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning.
Article in Frontiers in neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations.Frontiers in neuroinformatics · 2024Pooled it
- Article
- The Brain-Age Gap in Pediatric Dystonia: Neuroanatomical Deviations Inform Deep Brain Stimulation Outcomes.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Validation of an AI model for pediatric myelin maturation age assessment and benefits for junior radiologists.BMC medical imaging · 2026Article
- Aperiodic parameters of the fMRI power spectrum associate with preterm birth and neonatal age.Communications biology · 2026Article
- Brain age gap as a predictive biomarker that links aging, lifestyle, and neuropsychiatric health.Communications medicine · 2025Article
- Comparative analysis of supervised and self-supervised learning with small and imbalanced medical imaging datasets.Scientific reports · 2025Article
- A novel deep learning-based brain age prediction framework for routine clinical MRI scans.npj aging · 2025Article
- Redefining diagnostic lesional status in temporal lobe epilepsy with artificial intelligence.Brain : a journal of neurology · 2025Article
- MSLI-Net: retinal disease detection network based on multi-segment localization and multi-scale interaction.Frontiers in cell and developmental biology · 2025Article
- EEG-based brain age prediction in infants-toddlers: Implications for early detection of neurodevelopmental disorders.Developmental cognitive neuroscience · 2025Article
- Prediction of chronological age from resting-state EEG power in the first three years of life.medRxiv : the preprint server for health sciences · 2024Article
- Assessing brain maturation on neonatal MRI-do we need a more quantitative approach?European radiology · 2024Article
- Article
- Diffusion deep learning for brain age prediction and longitudinal tracking in children through adulthood.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Adding the third dimension: 3D convolutional neural network diagnosis of temporal lobe epilepsy.Brain communications · 2024Article
- Development and Evaluation of Deep Learning Models for Automated Estimation of Myelin Maturation Using Pediatric Brain MRI Scans.Radiology. Artificial intelligence · 2023Article
- Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis.Computer modeling in engineering & sciences : CMES · 2023Article
- Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Article
- Cervical Cell Image Classification-Based Knowledge Distillation.Biomimetics (Basel, Switzerland) · 2022Article
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7 authors.
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Abstract
Predicting brain age of children accurately and quantitatively can give help in brain development analysis and brain disease diagnosis. Traditional methods to estimate brain age based on 3D magnetic resonance (MR), T1 weighted imaging (T1WI), and diffusion tensor imaging (DTI) need complex preprocessing and extra scanning time, decreasing clinical practice, especially in children. This research aims at proposing an end-to-end AI system based on deep learning to predict the brain age based on routine brain MR imaging. We spent over 5 years enrolling 220 stacked 2D routine clinical brain MR T1-weighted images of healthy children aged 0 to 5 years old and randomly divided those images into training data including 176 subjects and test data including 44 subjects. Data augmentation technology, which includes scaling, image rotation, translation, and gamma correction, was employed to extend the training data. A 10-layer 3D convolutional neural network (CNN) was designed for predicting the brain age of children and it achieved reliable and accurate results on test data with a mean absolute deviation (MAE) of 67.6 days, a root mean squared error (RMSE) of 96.1 days, a mean relative error (MRE) of 8.2%, a correlation coefficient (
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