Deep Learning-Based Brain Age Prediction In Normal Aging And
Di: Everly
A new artificial intelligence tool developed by researchers at Mass General Brigham and Harvard Medical School uses a snapshot of a patient’s face to predict biological
Here, we leveraged the structural data from a large, heterogeneous dataset (N = 1464) to implement an interpretable 3D combined CNN model for brain-age prediction.In
Diffusion deep learning for brain age prediction and longitudinal

Background/Objectives: A multimodal brain age estimation model could provide enhanced insights into brain aging. However, effectively integrating multimodal neuroimaging
The concept of ‘brain age’, derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine
biomarker of brain aging: what insights have we gained? Front Neurol . 2019; 10: 789 Jonsson BA, Bjornsdottir G, Thorgeirsson TE, et al. Brain age . prediction using deep learning uncovers
- Age Prediction Based on Brain MRI Image: A Survey
- 3DCNN predicting brain age using diffusion tensor imaging
- Normal Brain Aging: Prediction of Age, Sex and White Matter
The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an
Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These
Deep learning for brain age estimation: A systematic review
Numerous studies have established that estimated brain age constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a
Human age prediction is an interesting and applicable issue in different fields. It can be based on various criteria such as face image, DNA methylation, chest plate radiographs,
Normal brain aging is accompanied by patterns of functional and structural change. Alzheimer’s disease (AD), a representative neurodegenerative disease, has been linked to accelerated
In a recent study, Chenzhong Yin and colleagues introduced a three-dimensional convolutional neural network (3D CNN) to estimate the BA of patients and to help with early
- Brain age prediction from MRI scans in neurodegenerative diseases
- Diffusion deep learning for brain age prediction and longitudinal
- New AI tool predicts biological age by looking at a face
- Brain age prediction using combined deep convolutional neural network
- Deep learning-based brain age prediction in normal aging and
Deep learning-based brain age prediction in normal aging and dementia Article 09 May 2022. Brain age prediction using combined deep convolutional neural network and multi
Brain aging is accompanied by patterns of functional and structural change. Alzheimer’s disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging.
Here, we developed a deep learning-based brain age prediction model using fluorodeoxyglucose (FDG) PET and structural MRI and tested how the brain age gap relates to degenerative
Here, we developed a deep learning-based brain age prediction model using fluorodeoxyglucose (FDG) PET and structural MRI and tested how the brain age gap relates to degenerative
A better understanding of normal human brain aging is required to better study age-related neurodegeneration including cognitive impairment. We propose an automatic deep
As a reliable genetic phenotype/biomarker, the brain age has been used to assess the risk of age-related neurodegenerative diseases (Franke and Gaser, 2019).A number of

Deep learning-based regres-sion techniques to predict brain age from magnetic res-onance imaging (MRI) scans have shown great accuracy recently. However, these methods are
By training models with structural and functional brain imaging data obtained serially over time, deep learning artificial intelligence can estimate the brain age gap—the difference between chronological age and predicted age.
Deep learning-based brain age prediction in normal aging and dementia. Nature Aging 2, 412–424. doi: 10.1038/s43587-022-00219-7 PubMed Abstract | CrossRef Full Text |
Deep learning (DL)-based prediction of biological age in the developing human from a brain magnetic resonance imaging (MRI) (“ brain age ”) may have important diagnostic
Neuroimaging-based brain age prediction using deep learning is gaining popularity. However, few studies have attempted to leverage diffusion tensor imaging (DTI) to
Machine learning algorithms can be trained to estimate age from brain structural MRI. Here, the authors introduce a new deep-learning-based age prediction approach, and
Study characteristics. This systematic review and meta-analysis included 28 studies published between 2019 and 2024, investigating the application of deep learning-based
Alzheimer’s disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging. Here, we developed a deep learning-based brain age prediction
In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex
Supporting: 12, Mentioning: 58 – Normal brain aging is accompanied by patterns of functional and structural change.Alzheimer’s disease (AD), a representative neurodegenerative disease, has
This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various
Here, we developed a deep learning-based brain age prediction model using fluorodeoxyglucose (FDG) PET and structural MRI and tested how the brain age gap relates to degenerative
Alzheimer’s disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging. Here, we developed a deep learning-based brain age prediction
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