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Age estimation from sleep studies using deep learning predicts life expectancy

Medicine and Health

Age estimation from sleep studies using deep learning predicts life expectancy

A. Brink-kjær, E. B. Leary, et al.

This groundbreaking study, conducted by a team of experts including Andreas Brink-Kjær and Eileen B. Leary, leverages deep neural networks to accurately estimate age and mortality risk from polysomnograms. With a remarkable mean absolute error of just 5.8 years, these models reveal crucial insights into how age estimation errors can significantly correlate with life expectancy.

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Playback language: English
Abstract
This study uses deep neural networks to estimate age and mortality risk from polysomnograms (PSGs). The models, trained on 2500 PSGs and tested on 10,699 PSGs, estimated age with a mean absolute error of 5.8 ± 1.6 years, significantly outperforming basic sleep scoring measures. Increased age estimation error (AEE) was associated with higher all-cause mortality, with a 10-year increment in AEE linked to an estimated 8.7-year decrease in life expectancy. Greater AEE correlated with increased sleep fragmentation, suggesting its importance as a biomarker.
Publisher
npj Digital Medicine
Published On
Nov 16, 2022
Authors
Andreas Brink-Kjær, Eileen B. Leary, Haoqi Sun, M. Brandon Westover, Katie L. Stone, Paul E. Peppard, Nancy E. Lane, Peggy M. Cawthon, Susan Redline, Poul Jennum, Helge B. D. Sorensen, Emmanuel Mignot
Tags
deep neural networks
age estimation
mortality risk
polysomnograms
biomarkers
sleep fragmentation
life expectancy

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