| Title: | Deep Learning for Predicting Refractive Error From Retinal Fundus Images |
| Journal: | Investigative Ophthalmology & Visual Science |
| Published: | 4 Jun 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/30025129/ |
| DOI: | https://doi.org/10.1167/iovs.18-23887 |
| URL: | ~Ehttps://watermark.silverchair.com/i1552-5783-59-7-2861.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAjwwggI4BgkqhkiG9w0BBwagggIpMIICJQIBADCCAh4GCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMSfbWpdQkZfwx-110AgEQgIIB76h6t8o55n-WBtsSXA1VTkajd1QTv3cwXK5FdVOU0D5OHBneO04X0xjDUyM4DxhV5PzsqCY_OTSoaPZsVegn0HjIpRfhiE8jep4ManpM0MsYKkmkYkxJHRkHe0qMywVuPD0gJreUxF1rPvv4AUbkhqcrngtkwZB3iVG4Si3JztqzpKe3rgtWhAyMXemiQ4_2d4XItJzLb85Lz4SbDh_IjgKMZn48DGoJ4KfpQZSaVllvow5JhEvOfwVrA3GszxLc5xUeNwZ5lW9mFzYW0INFCptDgIm2mgtFSd7Bd2nc_BzpsURFSW5x0ClPPmkLWcGX9Dt3S03ma_WYz7RzBl2qc-fjZ7cCTwj0gnt8V4momddv6Pskt-igkDeVKbqhnkMR4dBdtpzm4xMCH664GpZ2fb-VVB0W2zgrbu6W5ey3VbmC5nh7EabSgU1EQN42T2I_F5-uc06GLP_ozaroFtIjJcp-5cFhUA92Tj3BMoxf33VU2burivhzXcA6WzocIGKPGgyc8xtxUA1XEaKkOrvMFskp6W3KLl-Zw7M4eviVamyt1fRI7DNDtWA_lgpTRfGAddDgpNgkqtMzZffVHr-Sj_ufg3kOR2ULo8rxkf7-tQ2VCL2XAnK4djTc5zcUZgWBNKWCkJFi3XE1jeaXJ2sF5g~ |
| Citations: | 148 (49 in last 2 years) as of 8 Aug 2024 |
Publication 3656
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Abstract
Purpose: We evaluate how deep learning can be applied to extract novel information such as refractive error from retinal fundus imaging.</p>
Methods: Retinal fundus images used in this study were 45- and 30-degree field of view images from the UK Biobank and Age-Related Eye Disease Study (AREDS) clinical trials, respectively. Refractive error was measured by autorefraction in UK Biobank and subjective refraction in AREDS. We trained a deep learning algorithm to predict refractive error from a total of 226,870 images and validated it on 24,007 UK Biobank and 15,750 AREDS images. Our model used the "attention" method to identify features that are correlated with refractive error.</p>
Results: The resulting algorithm had a mean absolute error (MAE) of 0.56 diopters (95% confidence interval [CI]: 0.55-0.56) for estimating spherical equivalent on the UK Biobank data set and 0.91 diopters (95% CI: 0.89-0.93) for the AREDS data set. The baseline expected MAE (obtained by simply predicting the mean of this population) was 1.81 diopters (95% CI: 1.79-1.84) for UK Biobank and 1.63 (95% CI: 1.60-1.67) for AREDS. Attention maps suggested that the foveal region was one of the most important areas used by the algorithm to make this prediction, though other regions also contribute to the prediction.</p>
Conclusions: To our knowledge, the ability to estimate refractive error with high accuracy from retinal fundus photos has not been previously known and demonstrates that deep learning can be applied to make novel predictions from medical images.</p>
15 Keywords
- Adult
- Aged
- Algorithms
- Datasets as Topic
- Deep Learning
- Female
- Fundus Oculi
- Humans
- Male
- Middle Aged
- Refraction, Ocular
- Refractive Errors
- Retina
- Vision Tests
- Visual Fields
10 Authors
- Avinash V. Varadarajan
- Ryan Poplin
- Katy Blumer
- Christof Angermueller
- Joe Ledsam
- Reena Chopra
- Pearse A. Keane
- Greg S. Corrado
- Lily Peng
- Dale R. Webster
1 Application
| Application ID | Title |
|---|---|
| 17643 | Automated Detection of Ocular & Systemic Disease Via Retinal Fundus Imaging |
Enabling scientific discoveries that improve human health