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

Alan Le Goallec, Samuel Diai, Sasha Collin, Jean-Baptiste Prost, Théo Vincent and Chirag J. Patel ()
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Alan Le Goallec: Harvard Medical School
Samuel Diai: Harvard Medical School
Sasha Collin: Harvard Medical School
Jean-Baptiste Prost: Harvard Medical School
Théo Vincent: Harvard Medical School
Chirag J. Patel: Harvard Medical School

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-29525-9

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