ArticleMagnetic resonance imaging2019
Anatomical context improves deep learning on the brain age estimation task.
Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Structural MRI-Based Measures of Accelerated Brain Aging do not Moderate the Acute Antidepressant Response in Late-Life Depression.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2022Trial
- Anatomy-aware lymphoma lesion detection in whole-body PET/CT.Frontiers in oncology · 2026Article
- Brain Age Is Not a Significant Predictor of Relapse Risk in Late-Life Depression.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.AJNR. American journal of neuroradiology · 2024Article
- Predicting Age from White Matter Diffusivity with Residual Learning.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Article
- Brain structure ages-A new biomarker for multi-disease classification.Human brain mapping · 2024Article
- A deep neural network estimation of brain age is sensitive to cognitive impairment and decline.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms.Scientific reports · 2023Article
- Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information.Brain sciences · 2023Article
- An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Beyond the Limits of Visual Learning: Prediction of Time From Onset From Noncontrast CT of Acute Ischemic Stroke.Stroke · 2023Article
- The causal effect of HbA1c on white matter brain aging by two-sample Mendelian randomization analysis.Frontiers in neuroscience · 2023Article
- Attention-guided deep learning for gestational age prediction using fetal brain MRI.Scientific reports · 2022Article
- Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors.Frontiers in neurology · 2022Article
- Morphological analysis of subcortical structures for assessment of cognitive dysfunction in Parkinson's disease using multi-atlas based segmentation.Cognitive neurodynamics · 2021Article
- Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging.Frontiers in neuroscience · 2021Article
- Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression.Translational psychiatry · 2020Article
- From a deep learning model back to the brain-Identifying regional predictors and their relation to aging.Human brain mapping · 2020Article
- Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging.Neurobiology of aging · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network design to include traditional structural imaging features alongside deep convolutional ones and illustrate this approach on the task of imaging-based age prediction in two separate contexts: T1-weighted brain magnetic resonance imaging (MRI) (N = 5121, ages 4-96, healthy controls) and computed tomography (CT) of the head (N = 1313, ages 1-97, healthy controls). In brain MRI, we can predict age with a mean absolute error of 4.08 years by combining raw images along with engineered structural features, compared to 5.00 years using image-derived features alone and 8.23 years using structural features alone. In head CT, we can predict age with a median absolute error of 9.99 years combining features, compared to 11.02 years with image-derived features alone and 13.28 years with structural features alone. These results show that we can complement traditional feature estimation using deep learning to improve prediction tasks. As the field of medical image processing continues to integrate deep learning, it will be important to use the new techniques to complement traditional imaging features instead of fully displacing them.
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Registered trials
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