ReviewBriefings in functional genomics2025
A review of artificial intelligence-based brain age estimation and its applications for related diseases.
Review in Briefings in functional genomics, 2025. 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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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.
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Who cites it
8 citing papers in PubMed.
- Biological aging, circulating lipid biomarkers and pancreatic diseases: a prospective cohort study based on the UK Biobank.The journal of nutrition, health & aging · 2026Article
- Neonatal brain-age models in full- and preterm infants.Developmental cognitive neuroscience · 2026Article
- Local and global patterns support medical imaging as a biomarker of ageing.Communications medicine · 2026Article
- A Lightweight ScaleDense-Transformer Framework with Auxiliary Quantum-Inspired Bottleneck Module for Whole-Lifespan Brain Age Prediction.Brain sciences · 2026Article
- Brain age gap as biomarker linking cardiovascular diseases genetic susceptibility and causality.iScience · 2026Article
- Neonatal brain-age models in full- and preterm infants.bioRxiv : the preprint server for biology · 2026Article
- Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective.ArXiv · 2025Article
- Decoding brain aging trajectory: predictive discrepancies, genetic susceptibilities, and emerging therapeutic strategies.Frontiers in aging neuroscience · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The study of brain age has emerged over the past decade, aiming to estimate a person's age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change in the presence of brain-related diseases. Consequently, brain age also changes in affected individuals, making the brain age gap (BAG)-the difference between brain age and chronological age-a potential biomarker for brain health, early screening, and identifying age-related cognitive decline and disorders. With the recent successes of artificial intelligence in healthcare, it is essential to track the latest advancements and highlight promising directions. This review paper presents recent machine learning techniques used in brain age estimation (BAE) studies. Typically, BAE models involve developing a machine learning regression model to capture age-related variations in brain structure from imaging scans of healthy individuals and automatically predict brain age for new subjects. The process also involves estimating BAG as a measure of brain health. While we discuss recent clinical applications of BAE methods, we also review studies of biological age that can be integrated into BAE research. Finally, we point out the current limitations of BAE's studies.
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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.