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Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images
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Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images
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Journal Article
Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images
2022
Overview
With age, the prevalence of diseases such as fatty liver disease, cirrhosis, and type two diabetes increases. Approaches to both predict abdominal age and identify risk factors for accelerated abdominal age may ultimately lead to advances that will delay the onset of these diseases. We build an abdominal age predictor by training convolutional neural networks to predict abdominal age (or “AbdAge”) from 45,552 liver magnetic resonance images [MRIs] and 36,784 pancreas MRIs (R-Squared = 73.3 ± 0.6; mean absolute error = 2.94 ± 0.03 years). Attention maps show that the prediction is driven by both liver and pancreas anatomical features, and surrounding organs and tissue. Abdominal aging is a complex trait, partially heritable (h_g
2
= 26.3 ± 1.9%), and associated with 16 genetic loci (e.g. in
PLEKHA1
and
EFEMP1
), biomarkers (e.g body impedance), clinical phenotypes (e.g, chest pain), diseases (e.g. hypertension), environmental (e.g smoking), and socioeconomic (e.g education, income) factors.
Approaches to both determine abdominal age and identify risk factors for accelerated abdominal age will help delay the onset of several diseases. Here, the authors build an abdominal age predictor by training convolutional neural networks to predict abdominal age from liver and pancreas MRIs.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
