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Deep neural network-estimated age using optical coherence tomography predicts mortality.

Ruiye Chen1,2, Shiran Zhang3, Guankai Peng4

  • 1Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.

Geroscience
|September 21, 2023
PubMed
Summary

Optical coherence tomography (OCT) can predict biological age using a deep neural network (DNN), with an OCT age gap indicating mortality risk. A larger OCT age gap is linked to increased mortality risk.

Keywords:
Age gapDeep neural networkMortalityOptical coherence tomography

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Area of Science:

  • Ophthalmology
  • Gerontology
  • Biomedical Engineering

Background:

  • Biological age reflects physiological decline, distinct from chronological age.
  • Optical coherence tomography (OCT) is an imaging technique that provides high-resolution cross-sectional views of biological tissues.
  • Deep neural networks (DNNs) have shown promise in analyzing complex medical imaging data.

Purpose of the Study:

  • To develop a DNN model for predicting biological age using OCT images.
  • To investigate the association between the OCT-derived age gap and mortality risk.

Main Methods:

  • Utilized 84,753 OCT images from 53,159 UK Biobank participants.
  • Developed a DNN model using 12,631 3D-OCT images from healthy individuals.
  • Calculated the OCT age gap (OCT-predicted age - chronological age) for 44,618 participants.
  • Employed Cox regression models to analyze mortality risk associated with the OCT age gap.

Main Results:

  • The DNN model accurately predicted age with a mean absolute error of 3.27 years and a correlation of 0.85 with chronological age.
  • A 5-year increase in OCT age gap was associated with an 8% increased mortality risk (HR=1.08).
  • An OCT age gap >4 years increased mortality risk by 18% (HR=1.18), while an OCT age gap <-4 years decreased risk by 16% (HR=0.84).

Conclusions:

  • OCT imaging is a viable tool for predicting biological age with high accuracy.
  • The OCT age gap serves as a significant biomarker for predicting mortality risk.
  • This approach offers a novel method for assessing health status and longevity through retinal imaging.