Why the study?
Approaches to predict abdominal age and identify risk factors for accelerated abdominal aging may help delay the onset of age-associated conditions such as fatty liver disease, cirrhosis, and type two diabetes.
Can convolutional neural networks accurately predict abdominal age from liver and pancreas MRIs?
Can convolutional neural networks accurately predict abdominal age from liver and pancreas MRIs?
Deep learning models can accurately predict abdominal age from liver and pancreas MRIs, demonstrating that abdominal aging is a partially heritable trait associated with multiple genetic, clinical, and socioeconomic factors.
Abdominal age prediction from MRIs may refine aging biomarkers; leaves open prospective validation before clinical use.
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_g² = 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.
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Goallec et al. (2022) studied this question.
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