Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity

Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity

Abstract We derive and test a CT-based biological age model for predicting longevity, using an automated pipeline of explainable AI algorithms that quantifies skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs. We apply these AI tools to abdominal CT scans...

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Bibliographic Details
Main Authors: Perry J. Pickhardt, Michael W. Kattan, Matthew H. Lee, B. Dustin Pooler, Ayis Pyrros, Daniel Liu, Ryan Zea, Ronald M. Summers, John W. Garrett
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-56741-w
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https://doi.org/10.1038/s41467-025-56741-w

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