Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images

A, Le Goallec; Diai, S; Collin, S; Prost, J; Thouvenot, Vincent; Patel, Chirag

doi:10.1038/s41467-022-29525-9

Cited by 25 publications

(17 citation statements)

References 88 publications

(66 reference statements)

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“…Symmetrically, participants on the younger end of the chronological age distribution tend to be predicted older than they are. This bias does not seem to be biologically driven (Le Goallec et al, 2022). Rather it seems to be statistically driven, as the same 60-year-old individual will tend to be predicted younger in a cohort with an age range of 60-80 years, and to be predicted older in a cohort with an age range of 60-80.…”

Section: Methodsmentioning

confidence: 99%

“…We used the FUnctional Mapping and Annotation (FUMA) tool for identifying the independent loci of the GWAS results and mapping them to their closest genes (Watanabe et al, 2017) as we recently performed in a prior study on another organ dimension (Le Goallec et al, 2022). Specifically, we identify (1) the loci associated with each of the traits, and the (2) nearest protein coding genes.…”

Section: Identification Of Snps Associated With Accelerated Eye Agingmentioning

confidence: 99%

Section: Eye Age Definitionmentioning

confidence: 99%

“…Age predictors have recently been derived to measure accelerated aging(Horvath, 2013; Le Goallec et al, 2022) and of the eye (Poplin et al, 2018). Here, inspired by these approaches, we use machine learning algorithms to predict the age of the participant (also referred to as “chronological age”) from fundus and optical coherence tomography (OCT) images of the eye.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning of fundus and optical coherence tomography images enables identification of diverse genetic and environmental factors associated with eye aging

Diai

Collin

et al. 2021

Preprint

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With age, eyesight declines and the vulnerability to age-related eye diseases such as glaucoma, cataract, macular degeneration and diabetic retinopathy increases. With the aging of the global population, the prevalence of these diseases is projected to increase, leading to reduced quality of life and increased healthcare cost. In the following, we built an eye age predictor by training convolutional neural networks to predict age from 175,000 eye fundus and optical coherence tomography images (R-Squared=83.6+/-0.6%; root mean squared error=3.34+/-0.07 years). We used attention maps to identify the features driving the eye age prediction. We defined accelerated eye aging as the difference between eye age and chronological age and performed a genome wide association study [GWAS] on this phenotype. Accelerated eye aging is 28.2+-1.2% GWAS-heritable, and is associated with 255 single nucleotide polymorphisms in 122 genes (e.g HERC2, associated with eye pigmentation). Similarly, we identified biomarkers (e.g blood pressure), clinical phenotypes (e.g chest pain), diseases (e.g cataract), environmental variables (e.g sleep deprivation) and socioeconomic variables (e.g income) associated with our newly defined phenotype. Our predictor could be used to detect premature eye aging in patients, and to evaluate the effect of emerging rejuvenation therapies on eye health.

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Section: Methodsmentioning

confidence: 99%

Section: Identification Of Snps Associated With Accelerated Eye Agingmentioning

confidence: 99%

Section: Eye Age Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning of fundus and optical coherence tomography images enables identification of diverse genetic and environmental factors associated with eye aging

Diai

Collin

et al. 2021

Preprint

Self Cite

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“…NAFLD is linked not just to metabolic abnormalities but also bad lifestyle choices ( 5 ). Also, population aging accelerates the progression of NAFLD ( 10 , 11 ). Thus as evidenced by the abdominal age predictor, AbdAge model, which was developed on liver MRI images and revealed that with advanced age, the liver becomes darker, its volume diminishes, and blood flow declines ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Association between smoking and non-alcoholic fatty liver disease in Southeast Asia

Mumtaz

Hameed²,

Sangah

et al. 2022

Front. Public Health

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An estimated 8 million people die each year from tobacco smoking, with an increasing frequency recently being observed in Southeast Asian countries, which is a preventable risk factor for mortality. NAFLD, fibrosis, advancement of hepatocellular carcinoma, and prognosis for those with severe liver disease are all negatively influenced. NAFLD and cigarette usage seem to be a direct link. Oxidative stress and oncogenic signals have been implicated in cancer development in animal models and human clinical trials. The elevated risk of cardiovascular disease and malignancies in those with steatohepatitis and those who have had liver transplants is exacerbated by smoking. We found that smoking cessation may increase treatment response and fibrosis regression rates, decrease hepatocellular carcinoma incidence, and improve liver transplant outcomes. In the last segment, we'll look at electronic cigarettes, a hot subject in public health right now, as well as additional repercussions of smoking.

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Brain structure ages—A new biomarker for multi‐disease classification

Nguyen,

Clément,

Mansencal

et al. 2024

Human Brain Mapping

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Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some insights into neurological diseases. In neuroimaging, predicted brain age is widely used to analyze different diseases. However, using only the brain age gap information (i.e., the difference between the chronological age and the estimated age) can be not enough informative for disease classification problems. In this paper, we propose to extend the notion of global brain age by estimating brain structure ages using structural magnetic resonance imaging. To this end, an ensemble of deep learning models is first used to estimate a 3D aging map (i.e., voxel‐wise age estimation). Then, a 3D segmentation mask is used to obtain the final brain structure ages. This biomarker can be used in several situations. First, it enables to accurately estimate the brain age for the purpose of anomaly detection at the population level. In this situation, our approach outperforms several state‐of‐the‐art methods. Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure. This feature can be used in a multi‐disease classification task for an accurate differential diagnosis at the subject level. Finally, the brain structure age deviations of individuals can be visualized, providing some insights about brain abnormality and helping clinicians in real medical contexts.

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Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images

Cited by 25 publications

References 88 publications

Deep learning of fundus and optical coherence tomography images enables identification of diverse genetic and environmental factors associated with eye aging

Deep learning of fundus and optical coherence tomography images enables identification of diverse genetic and environmental factors associated with eye aging

Association between smoking and non-alcoholic fatty liver disease in Southeast Asia

Brain structure ages—A new biomarker for multi‐disease classification

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