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

Medicine and Health

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

A. L. Goallec, S. Diai, et al.

This exciting research, conducted by Alan Le Goallec, Samuel Diai, Sasha Collin, Jean-Baptiste Prost, Théo Vincent, and Chirag J. Patel, presents a groundbreaking abdominal age predictor developed with convolutional neural networks. It showcases a high accuracy in predicting abdominal aging by analyzing liver and pancreas MRIs, illuminating key anatomical features and associated genetic factors.

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Playback language: English
Abstract
This study develops an abdominal age predictor using convolutional neural networks trained on liver and pancreas MRIs from the UK Biobank. The model achieves high accuracy (R-Squared = 73.3±0.6; mean absolute error = 2.94 ± 0.03 years) and identifies anatomical features and surrounding organs/tissues as key predictors. Abdominal aging's heritability is estimated, and associated genetic loci, biomarkers, clinical phenotypes, diseases, environmental, and socioeconomic factors are identified.
Publisher
NATURE COMMUNICATIONS
Published On
Apr 13, 2022
Authors
Alan Le Goallec, Samuel Diai, Sasha Collin, Jean-Baptiste Prost, Théo Vincent, Chirag J. Patel
Tags
abdominal age predictor
convolutional neural networks
MRIs
hereditary factors
anatomical features
genetic loci
biomarkers

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