ArticleScientific reports2023
Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- Accelerated Brain Aging in Young Women with Posttraumatic Stress Disorder.Experimental neurobiology · 2026Article
- Depression and aging: insights from brain age prediction models.Psychological medicine · 2026Article
- Structural connectome analysis using a graph-based deep model for prediction of non-imaging variables.Frontiers in neuroscience · 2026Article
- Real-world federated learning for brain imaging scientists.Frontiers in digital health · 2026Article
- Article
- Potential vulnerability and resilience to accelerated brain aging in women exposed to stressful life events: insights from the brain age prediction model.Neurobiology of stress · 2025Article
- Dynamic Brain Age Modeling Identifies Network-Specific Cognitive Deficits in Schizophrenia.Research square · 2025Article
- Structural Connectome Analysis using a Graph-based Deep Model for Prediction of Non-Imaging Variables.bioRxiv : the preprint server for biology · 2025Article
- Brain age prediction from MRI scans in neurodegenerative diseases.Current opinion in neurology · 2025Review
- Functional Brain Age Acceleration from Dynamic and Static Connectivity Predicts Working Memory and Attention Deficits in Schizophrenia.medRxiv : the preprint server for health sciences · 2025Article
- Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters.Endocrinology and metabolism (Seoul, Korea) · 2025Article
- Integrating clinical indications and patient demographics for multilabel abnormality classification and automated report generation in 3D chest CT scans.Frontiers in radiology · 2025Article
- Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.Tomography (Ann Arbor, Mich.) · 2024Review
- Predicting brain age using Tri-UNet and various MRI scale features.Scientific reports · 2024Article
- Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection.Brain informatics · 2024Article
- Exploring the Potential of Machine Learning Algorithms to Improve Diffusion Nuclear Magnetic Resonance Imaging Models Analysis.Journal of medical physicsArticle
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9 authors.
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Abstract
The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer's disease groups by identifying variances in brain age gaps between them, highlighting the algorithm's potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.
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