Deep-learning-based prediction of late age-related macular degeneration progression

Abstract

Both genetic and environmental factors influence the etiology of age-related macular degeneration (AMD), a leading cause of blindness. AMD severity is primarily measured by images of the fundus of the retina and recently developed machine learning methods can successfully predict AMD progression using image data. However, none of these methods have used both genetic and image data for predicting AMD progression. Here we used both genotypes and fundus images to predict whether an eye had progressed to late AMD with a modified deep convolutional neural network. In total, we used 31,262 fundus images and 52 AMD-associated genetic variants from 1,351 subjects from the Age-Related Eye Disease Study, which provided disease severity phenotypes and fundus images available at baseline and follow-up visits over a period of 12 years. Our results showed that fundus images coupled with genotypes could predict late AMD progression with an averaged area-under-the-curve value of 0.85 (95% confidence interval 0.83–0.86). The results using fundus images alone showed an averaged area under the receiver operating characteristic curve value of 0.81 (95% confidence interval 0.80–0.83). We implemented our model in a cloud-based application for individual risk assessment.

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Fig. 1: Receiver operator characteristic curves of the prediction of late AMD progression time exceeding the inquired years for four models.
Fig. 2: Saliency maps for left eye of subject 1 over 5.9 years.
Fig. 3: Saliency maps for left eye of subject 2 over the first 5.8 years.
Fig. 4: Saliency maps for left eye of subject 3 over 12 years.
Fig. 5

Data availability

All the phenotype data and fundus images of AREDS participants required are available from dbGap (accession phs000001.v3.p1). The genotype data on AREDS subjects have been reported earlier9 and are available from dbGap (accession phs001039.v1.p1). The UK Biobank test dataset was obtained from UK Biobank (application number 43252).

Code availability

The prediction models with Python implementation and a detailed tutorial are available at https://github.com/QiYanPitt/AMDprogressCNN and a web-based graphical user interface is also available at http://www.pitt.edu/~qiy17/amdprediction.html.

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Acknowledgements

H.H. was partially supported by NSF IIS 1852606 and IIS 1837956. A.S. was supported by NEI Intramural Research Program grant number ZIAEY000546. The independent validation has been conducted using the UK Biobank Resource under Application Number 43252.

Author information

Q.Y., Y.D. and W.C. conceived the project and supervised it. Q.Y. performed data processing and analysis. Q.Y. and H.X. designed the study. Q.Y., A.S. and E.Y.C. interpreted the data. Q.Y. and D.E.W. wrote the paper and Y.D., W.C., H.H., A.S. and E.Y.C. commented on the manuscript.

Correspondence to Qi Yan or Ying Ding or Wei Chen.

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Yan, Q., Weeks, D.E., Xin, H. et al. Deep-learning-based prediction of late age-related macular degeneration progression. Nat Mach Intell 2, 141–150 (2020). https://doi.org/10.1038/s42256-020-0154-9

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