| Title: | Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images |
| Journal: | Nature Communications |
| Published: | 13 Apr 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35418184/ |
| DOI: | https://doi.org/10.1038/s41467-022-29525-9 |
| URL: | https://www.nature.com/articles/s41467-022-29525-9.pdf |
| Citations: | 24 (19 in last 2 years) as of 8 Aug 2024 |
Publication 12415
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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.</p>
6 Keywords
- Deep Learning
- Image Processing, Computer-Assisted
- Liver
- Magnetic Resonance Imaging
- Neural Networks, Computer
- Pancreas
6 Authors
- Alan Le Goallec
- Samuel Diai
- Sasha Collin
- Jean-Baptiste Prost
- Théo Vincent
- Chirag J. Patel
1 Application
| Application ID | Title |
|---|---|
| 52887 | Leveraging medical images and deep learning to characterize biological age |
Enabling scientific discoveries that improve human health