Predicting Age From Optical Coherence Tomography Scans With Deep Learning.

Journal Article (Journal Article)

Purpose

To assess whether age can be predicted from deep learning analysis of peripapillary spectral-domain optical coherence tomography (SD-OCT) B-scans and to determine the importance of specific retinal areas on the predictions.

Methods

Deep learning (DL) convolutional neural networks were developed to predict chronological age in healthy subjects using peripapillary SD-OCT B-scan images. Models were built using the whole B-scan, as well as using specific regions through image ablation. Cross-validation was used for training and testing the model. Mean absolute error (MAE) and correlations between predicted and observed age were used to evaluate model performance.

Results

A total of 7271 images from 542 eyes of 278 healthy subjects were included. DL predictions of age using the whole B-scan were strongly correlated with chronological age (MAE = 5.82 years; r = 0.860, P < 0.001). The model also accurately discriminated between the lowest and highest tertiles of age, with an area under the receiver operating characteristic curve of 0.962. In general, class activation maps tended to show a diffuse pattern of activation throughout the scan image. For specific structures of the B-scan, the layers with the strongest correlations with chronological age were the choroid and vitreous (both r = 0.736), whereas retinal nerve fiber layer had the lowest correlation (r = 0.492).

Conclusions

A DL algorithm was able to accurately predict age from whole peripapillary SD-OCT B-scans.

Translational relevance

DL models applied to SD-OCT scans suggest that aging appears to affect several layers in the posterior eye segment.

Full Text

Duke Authors

Cited Authors

  • Shigueoka, LS; Mariottoni, EB; Thompson, AC; Jammal, AA; Costa, VP; Medeiros, FA

Published Date

  • January 7, 2021

Published In

Volume / Issue

  • 10 / 1

Start / End Page

  • 12 -

PubMed ID

  • 33510951

Pubmed Central ID

  • PMC7804495

Electronic International Standard Serial Number (EISSN)

  • 2164-2591

International Standard Serial Number (ISSN)

  • 2164-2591

Digital Object Identifier (DOI)

  • 10.1167/tvst.10.1.12

Language

  • eng