Brain age prediction of healthy subjects on anatomic MRI with deep learning: going beyond with an "explainable AI" mindset

Hérent, Paul; Jégou, Simon; Wainrib, Gilles; Clozel, Thomas

doi:10.1101/413302

Cited by 7 publications

(2 citation statements)

References 45 publications

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“…These models are able to learn complex relations between input data and desired outcomes. Recent studies (11,12) were able to demonstrate that CNN models can be successfully applied in brain MRI-based age prediction (5,6).…”

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confidence: 99%

Gray Matter Age Prediction as a Biomarker for Risk of Dementia

Wang

Knol

Tiulpin

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

175

146

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The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model to predict brain age trained on 3,688 dementia-free participants of the Rotterdam Study (mean age 66 ± 11 y, 55% women). Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for age, sex, intracranial volume, GM volume, hippocampal volume, white matter hyperintensities, years of education, and APOE e4 allele carriership. Additionally, we computed the attention maps, which shows which regions are important for age prediction. Logistic regression and Cox proportional hazard models showed that the age gap was significantly related to incident dementia (odds ratio [OR] = 1.11 and 95% confidence intervals [CI] = 1.05-1.16; hazard ratio [HR] = 1.11, and 95% CI = 1.06-1.15, respectively). Attention maps indicated that GM density around the amygdala and hippocampi primarily drove the age estimation. We showed that the gap between predicted and chronological brain age is a biomarker, complimentary to those that are known, associated with risk of dementia, and could possibly be used for early-stage dementia risk screening. deep learning | dementia | age prediction | magnetic resonance imaging | voxel-based morphometry T he human brain continuously changes throughout the entire lifespan. These changes partially reflect a normal aging process and are not necessarily pathological (1). However, neurodegenerative diseases, including dementia, also affect brain structure and function (2, 3). Therefore, a better understanding and modeling of normal brain aging can help to disentangle these two processes and improve the detection of early-stage neurodegeneration.Age prediction models based on brain MRI are a popular trend in neuroscience (4-7). The difference between predicted and chronological age is thought to serve as an important biomarker reflecting pathological processes in the brain. Several recent studies showed the relation between accelerated brain aging and various disorders, such as Alzheimer's disease (8), schizophrenia, epilepsy, or diabetes (7,9,10).In recent years, CNNs have become the methodology of choice for analyzing medical images. These models are able to learn complex relations between input data and desired outcomes. Recent studies (11, 12) were able to demonstrate that CNN models can be successfully applied in brain MRI-based age prediction (5, 6).Although cross-sectional studies have suggested that the gap between predicted and chronological age may serve as a biomarker for dementia diagnosis, it remains unclear whether this is also t...

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mentioning

confidence: 99%

Gray Matter Age Prediction as a Biomarker for Risk of Dementia

Wang

Knol

Tiulpin

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

175

146

View full text Add to dashboard Cite

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“…This occurs when CNNs learn patterns that are specific to the training set and do not generalize to the overall population. In one case study, for example, a model trained to predict a patient's age based on MRI images was found to have learned the shape of the head rather than the content of the scan itself [28]. The challenge of overfitting is compounded by CNNs' inherent 'black box' quality.…”

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confidence: 99%

Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress

et al. 2019

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As demand for freshwater increases while supply remains stagnant, the critical need for sustainable water use in agriculture has led the EPA Strategic Plan to call for new technologies that can optimize water allocation in real-time. This work assesses the use of cloud-based artificial intelligence to detect early indicators of water stress across six container-grown ornamental shrub species. Near-infrared images were previously collected with modified Canon and MAPIR Survey II cameras deployed via a small unmanned aircraft system (sUAS) at an altitude of 30 meters. Cropped images of plants in no, low-, and high-water stress conditions were split into four-fold cross-validation sets and used to train models through IBM Watson’s Visual Recognition service. Despite constraints such as small sample size (36 plants, 150 images) and low image resolution (150 pixels by 150 pixels per plant), Watson generated models were able to detect indicators of stress after 48 hours of water deprivation with a significant to marginally significant degree of separation in four out of five species tested (p < 0.10). Two models were also able to detect indicators of water stress after only 24 hours, with models trained on images of as few as eight water-stressed Buddleia plants achieving an average area under the curve (AUC) of 0.9884 across four folds. Ease of pre-processing, minimal amount of training data required, and outsourced computation make cloud-based artificial intelligence services such as IBM Watson Visual Recognition an attractive tool for agriculture analytics. Cloud-based artificial intelligence can be combined with technologies such as sUAS and spectral imaging to help crop producers identify deficient irrigation strategies and intervene before crop value is diminished. When brought to scale, frameworks such as these can drive responsive irrigation systems that monitor crop status in real-time and maximize sustainable water use.

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Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI

Lin

Lee

Huang

et al. 2022

Brain Imaging and Behavior

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Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

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Brain age prediction of healthy subjects on anatomic MRI with deep learning: going beyond with an "explainable AI" mindset

Cited by 7 publications

References 45 publications

Gray Matter Age Prediction as a Biomarker for Risk of Dementia

Gray Matter Age Prediction as a Biomarker for Risk of Dementia

Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI

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