ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2023
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.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- 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
- Editorial for "Infant Brain Age Estimation With T1w/T2w Ratio MRI: A Myelination-Aware Deep Learning Approach".Journal of magnetic resonance imaging : JMRI · 2026Article
- Article
- Myelination-attention-empowered deep learning model improved brain age prediction in children below 2 years of age.Pediatric radiology · 2026Article
- Dual-Modal Deep Learning with In-Domain Training and Attention for Infant Brain Myelination Prediction.Neuroinformatics · 2026Article
- Validation of an AI model for pediatric myelin maturation age assessment and benefits for junior radiologists.BMC medical imaging · 2026Article
- Decoding Brain Development and Aging: Pioneering Insights From MRI Techniques.Investigative radiology · 2025Review
- Development and Evaluation of Deep Learning Models for Automated Estimation of Myelin Maturation Using Pediatric Brain MRI Scans.Radiology. Artificial intelligence · 2023Article
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Automatic detection of punctate white matter lesions in infants using deep learning of composite images from two cases.Scientific reports · 2023Article
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18 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeMyelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted images (T2WIs) has been widely used in radiology. This study aimed to simulate a rule-based workflow using a stacked deep learning model and evaluate age estimation accuracy.
methodsThe age estimation system involved two stacked neural networks: a target network-to extract five myelination-related images from the whole brain, and an age estimation network from extracted T1- and T2WIs separately. A dataset was constructed from 119 children aged below 2 years with two MRI systems. A four-fold cross-validation method was adopted. The correlation coefficient (CC), mean absolute error (MAE), and root mean squared error (RMSE) of the corrected chronological age of full-term birth, as well as the mean difference and the upper and lower limits of 95% agreement, were measured. Generalization performance was assessed using datasets acquired from different MR images. Age estimation was performed in Sturge-Weber syndrome (SWS) cases.
resultsThere was a strong correlation between estimated age and corrected chronological age (MAE: 0.98 months; RMSE: 1.27 months; and CC: 0.99). The mean difference and standard deviation (SD) were -0.15 and 1.26, respectively, and the upper and lower limits of 95% agreement were 2.33 and -2.63 months. Regarding generalization performance, the performance values on the external dataset were MAE of 1.85 months, RMSE of 2.59 months, and CC of 0.93. Among 13 SWS cases, 7 exceeded the limits of 95% agreement, and a proportional bias of age estimation based on myelination acceleration was exhibited below 12 months of age (P = 0.03).
conclusionStacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.
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