Predicting conversion to wet age-related macular degeneration using deep learning
Open accessAbstract
Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the second eye. By combining models based on three-dimensional (3D) optical coherence tomography images and corresponding automatic tissue maps, our system predicts conversion to exAMD within a clinically actionable 6-month time window, achieving a per-volumetric-scan sensitivity of 80% at 55% specificity, and 34% sensitivity at 90% specificity. This level of performance corresponds to true positives in 78% and 41% of individual eyes, and false positives in 56% and 17% of individual eyes at the high sensitivity and high specificity points, respectively. Moreover, we show that automatic tissue segmentation can identify anatomical changes before conversion and high-risk subgroups. This AI system overcomes substantial interobserver variability in expert predictions, performing better than five out of six experts, and demonstrates the potential of using AI to predict disease progression.
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CNNs can diagnose or predict the progression of center-involved diabetic macular edema, age-related macular degeneration, glaucoma, manifest visual field loss, childhood blindness, and others.
“Similarly, they can diagnose or predict the progression of center-involved diabetic macular edema 109 , age-related macular degeneration 107,110 , glaucoma 107,111 , manifest visual field loss 112 , childhood blindness 113 , and others.”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [110]
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- Authors
- Yim, J., Chopra, R., Spitz, T., Winkens, J., Obika, A., Kelly, C., Askham, H., Lukic, M., Huemer, J., Fasler, K., Moraes, G., Meyer, C., Wilson, M., Dixon, J., Hughes, C., Rees, G., Khaw, P. T., Karthikesalingam, A., King, D., Hassabis, D., Suleyman, M., Back, T., Ledsam, J. R., Keane, P. A., & De Fauw, J.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- NIHR Moorfields Biomedical Research Centre (GB)
- Google (United Kingdom) (GB)
- University College London (GB)
- National Institute for Health and Care Research (GB)
- Corresponding author
- Ledsam, Joseph R.
- Keane, Pearse A.
- ORCID
- Yim, Jason
- Chopra, Reena
- Spitz, Terry
- Kelly, Christopher
- Askham, Harry
- Lukic, Marko
- Huemer, Josef
- Fasler, Katrin
- Moraes, Gabriella
- Meyer, Clemens
- Hughes, Cian
- Rees, Geraint
- Khaw, Peng T.
- Karthikesalingam, Alan
- King, Dominic
- Hassabis, Demis
- Suleyman, Mustafa
- Back, Trevor
- Ledsam, Joseph R.
- Keane, Pearse A.
- De Fauw, Jeffrey
- Venue
- Nature Medicine
- Published
- 2020-05-18
- Volume
- 26
- Issue
- 6
- Pages
- 892-899
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W3023998086
- Cited by
- 297
- References
- 60
- Citations by year
- 2026: 32 · 2025: 50 · 2024: 41 · 2023: 56 · 2022: 48 · 2021: 51 · 2020: 15 · 2019: 2 · 2018: 1
- FWCI
- 20.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- College of Optometrists
- National Institute for Health and Care Research (CS-2014-14-023, CL-2010-18-004, NF-SI-0512-10101, II-C2-0713-20004)
- Academy of Medical Sciences (AMS-SGCL6-Keane)
- Moorfields Eye Hospital NHS Foundation Trust
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- neovascular age-related macular degeneration, disease progression prediction, 3D optical coherence tomography, deep learning, interobserver variability, high-risk subgroups, retinal anatomy
- MeSH terms
- Deep Learning, Aged, Aged, 80 and over, Female, Humans, Macular Degeneration, Male, Prognosis, Disease Progression, Imaging, Three-Dimensional, Tomography, Optical Coherence, Early Diagnosis, Geographic Atrophy, Wet Macular Degeneration, Early Medical Intervention
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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