ArticleScientific reports2023
Toward MR protocol-agnostic, unbiased brain age predicted from clinical-grade MRIs.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Improving racial fairness in brain age models using style-transfer synthesis.bioRxiv : the preprint server for biology · 2026Article
- Brain-age in ultra-low-field MRI: How well does it work?Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Regional brain aging patterns reveal disease-specific pathways of neurodegeneration.medRxiv : the preprint server for health sciences · 2025Article
- Does restrictive anorexia nervosa impact brain aging? A machine learning approach to estimate age based on brain structure.Computers in biology and medicine · 2025Article
- A novel deep learning-based brain age prediction framework for routine clinical MRI scans.npj aging · 2025Article
- Brain age in multiple sclerosis: a study with deep learning and traditional machine learning.Brain communications · 2025Article
- Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.Tomography (Ann Arbor, Mich.) · 2024Review
- Brain aging among individuals with trigeminal neuralgia.Neurobiology of pain (Cambridge, Mass.)Article
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Abstract
The difference between the estimated brain age and the chronological age ('brain-PAD') could become a clinical biomarker. However, most brain age models were developed for research-grade high-resolution T1-weighted MRIs, limiting their applicability to clinical-grade MRIs from various protocols. We adopted a dual-transfer learning strategy to develop a model agnostic to modality, resolution, or slice orientation. We retrained a convolutional neural network (CNN) using 6281 clinical MRIs from 1559 patients, among 7 modalities and 8 scanner models. The CNN was trained to estimate brain age from synthetic research-grade magnetization-prepared rapid gradient-echo MRIs (MPRAGEs) generated by a 'super-resolution' method. The model failed with T2-weighted Gradient-Echo MRIs. The mean absolute error (MAE) was 5.86-8.59 years across the other modalities, still higher than for research-grade MRIs, but comparable between actual and synthetic MPRAGEs for some modalities. We modeled the "regression bias" in brain age, for its correction is crucial for providing unbiased summary statistics of brain age or for personalized brain age-based biomarkers. The bias model was generalizable as its correction eliminated any correlation between brain-PAD and chronological age in new samples. Brain-PAD was reliable across modalities. We demonstrate the feasibility of brain age predictions from arbitrary clinical-grade MRIs, thereby contributing to personalized medicine.
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