Ontology highlight
ABSTRACT:
SUBMITTER: Le Goallec A
PROVIDER: S-EPMC9007982 | biostudies-literature | 2022 Apr
REPOSITORIES: biostudies-literature
Le Goallec Alan A Diai Samuel S Collin Sasha S Prost Jean-Baptiste JB Vincent Théo T Patel Chirag J CJ
Nature communications 20220413 1
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 ...[more]