Article

Using deep learning to predict age from liver and pancreas magnetic resonance images allows the identification of genetic and non-genetic factors associated with abdominal aging

2021-06-29

Abstract excerpt

With age, abdominal organs and tissue undergo important changes. For example, liver volume declines, fatty replacement increases in the pancreas, and patients become more vulnerable to age-related diseases such as non-alcoholic fatty liver disease, alcoholic liver disease, hepatitis, fibrosis, cirrhosis, type two diabetes, cancer, gallstones and inflammatory pancreatic disease. Detecting early abdominal aging and...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0b1177a6-1f93-5f1d-985a-fb159caa680a
DOI: 10.1101/2021.06.24.21259492

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Using deep learning to predict age from liver and pancreas magnetic resonance images allows the identification of genetic and non-genetic factors associated with abdominal agingDOI 10.1101/2021.06.24.21259492Read

Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning.Semantic proximity Read

Genetic architecture of 11 abdominal organ traits derived from abdominal MRI using deep learningSemantic proximity Read

Deep Learning Reveals Liver MRI Features Associated With PNPLA3 I148M in Steatotic Liver Disease.Semantic proximity Read

Analyzing the multidimensionality of biological aging with the tools of deep learning across diverse image-based and physiological indicators yields robust age predictorsSemantic proximity Read

Imaging-based organ-specific aging clock predicts human diseases and mortalitySemantic proximity Read

Histological aging signatures enable tissue-specific disease prediction from bloodSemantic proximity Read

Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageingSemantic proximity Read

Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders.Semantic proximity Read

Refining cardiometabolic risk assessment using MRI-derived pancreas volume and fat content: insights from the NAKO and UK BiobankSemantic proximity Read

Deep learning models reading clinical data and liver omics strongly distinguish NASH from steatosis and suggest new genes involved in liver disease severitySemantic proximity Read

Machine learning enables new insights into genetic contributions to liver fat accumulationSemantic proximity Read

Novel biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevitySemantic proximity Read