Predicting conversion to wet age-related macular degeneration using deep learning

[1]  Min Kim,et al.  Neovascularization in Fellow Eye of Unilateral Neovascular Age-Related Macular Degeneration According to Different Drusen Types. , 2019, American journal of ophthalmology.

[2]  C. Wykoff,et al.  Simultaneous Inhibition of Angiopoietin-2 and Vascular Endothelial Growth Factor-A with Faricimab in Diabetic Macular Edema: BOULEVARD Phase 2 Randomized Trial. , 2019, Ophthalmology.

[3]  P. Campochiaro,et al.  The Port Delivery System with Ranibizumab for Neovascular Age-Related Macular Degeneration: Results from the Randomized Phase 2 Ladder Clinical Trial. , 2019, Ophthalmology.

[4]  Suman V. Ravuri,et al.  A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury , 2019, Nature.

[5]  G. Corrado,et al.  End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography , 2019, Nature Medicine.

[6]  Anna Goldenberg,et al.  What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use , 2019, MLHC.

[7]  Aaron Y. Lee,et al.  The Moorfields AMD Database Report 2 - Fellow Eye Involvement with Neovascular Age-related Macular Degeneration , 2019, bioRxiv.

[8]  Daniel L. Rubin,et al.  A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers , 2019, ArXiv.

[9]  Sergio Gomez Colmenarejo,et al.  TF-Replicator: Distributed Machine Learning for Researchers , 2019, ArXiv.

[10]  Daniel B. Russakoff,et al.  Deep Learning for Prediction of AMD Progression: A Pilot Study. , 2019, Investigative ophthalmology & visual science.

[11]  E. Souied,et al.  Natural History of Treatment-Naïve Quiescent Choroidal Neovascularization in Age-Related Macular Degeneration Using OCT Angiography. , 2018, Ophthalmology. Retina.

[12]  Geraint Rees,et al.  Clinically applicable deep learning for diagnosis and referral in retinal disease , 2018, Nature Medicine.

[13]  T. Bek,et al.  Incidence and risk factors for neovascular age-related macular degeneration in the fellow eye , 2018, Graefe's Archive for Clinical and Experimental Ophthalmology.

[14]  P. Rosenfeld,et al.  Interpretation of Subretinal Fluid Using OCT in Intermediate Age-Related Macular Degeneration. , 2018, Ophthalmology. Retina.

[15]  Bianca S. Gerendas,et al.  Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence. , 2018, Investigative ophthalmology & visual science.

[16]  R. Poplin,et al.  Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning , 2018, Nature Biomedical Engineering.

[17]  Chen Sun,et al.  Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification , 2017, ECCV.

[18]  Glenn J Jaffe,et al.  Consensus Definition for Atrophy Associated with Age-Related Macular Degeneration on OCT: Classification of Atrophy Report 3. , 2017, Ophthalmology.

[19]  Jonathan Krause,et al.  Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy , 2017, Ophthalmology.

[20]  Sophie Kubach,et al.  Natural History of Subclinical Neovascularization in Nonexudative Age-Related Macular Degeneration Using Swept-Source OCT Angiography. , 2017, Ophthalmology.

[21]  Gregory S. Corrado,et al.  Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning , 2017, Nature Biomedical Engineering.

[22]  U. Schmidt-Erfurth,et al.  Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging. , 2017, Investigative ophthalmology & visual science.

[23]  Tommaso Rossi,et al.  PREDICTIVE FACTORS FOR DEVELOPMENT OF NEOVASCULAR AGE-RELATED MACULAR DEGENERATION: A Spectral-Domain Optical Coherence Tomography Study , 2017, Retina.

[24]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[25]  C. Curcio,et al.  Clinical Characteristics, Choroidal Neovascularization, and Predictors of Visual Outcomes in Acquired Vitelliform Lesions. , 2016, American journal of ophthalmology.

[26]  Sandra S Stinnett,et al.  Optical Coherence Tomography Reflective Drusen Substructures Predict Progression to Geographic Atrophy in Age-related Macular Degeneration. , 2016, Ophthalmology.

[27]  Jesse J. Jung,et al.  Associations Between Retinal Pigment Epithelium and Drusen Volume Changes During the Lifecycle of Large Drusenoid Pigment Epithelial Detachments , 2016, Investigative ophthalmology & visual science.

[28]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Geraint Rees,et al.  Automated analysis of retinal imaging using machine learning techniques for computer vision , 2016, F1000Research.

[30]  G. Ying,et al.  Pseudodrusen and Incidence of Late Age-Related Macular Degeneration in Fellow Eyes in the Comparison of Age-Related Macular Degeneration Treatments Trials. , 2016, Ophthalmology.

[31]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[32]  Ruikang K. Wang,et al.  Original articleOptical Coherence Tomography Angiography of Asymptomatic Neovascularization in Intermediate Age-Related Macular Degeneration , 2016 .

[33]  P. Rosenfeld,et al.  Drusen Volume as a Predictor of Disease Progression in Patients With Late Age-Related Macular Degeneration in the Fellow Eye. , 2016, Investigative ophthalmology & visual science.

[34]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[35]  D. Cook,et al.  Incidence of Late-Stage Age-Related Macular Degeneration in American Whites: Systematic Review and Meta-analysis. , 2015, American journal of ophthalmology.

[36]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

[37]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[38]  C. Bunce,et al.  The neovascular age-related macular degeneration database: report 2: incidence, management, and visual outcomes of second treated eyes. , 2014, Ophthalmology.

[39]  R. Klein,et al.  Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. , 2014, The Lancet. Global health.

[40]  R. Avery,et al.  Incidence of choroidal neovascularization in the fellow eye in the comparison of age-related macular degeneration treatments trials. , 2013, Ophthalmology.

[41]  Sina Farsiu,et al.  Progression of intermediate age-related macular degeneration with proliferation and inner retinal migration of hyperreflective foci. , 2013, Ophthalmology.

[42]  Stefanie G. Schuman,et al.  Spatial correlation between hyperpigmentary changes on color fundus photography and hyperreflective foci on SDOCT in intermediate AMD. , 2012, Investigative ophthalmology & visual science.

[43]  R. Guymer,et al.  Delay to treatment and visual outcomes in patients treated with anti-vascular endothelial growth factor for age-related macular degeneration. , 2012, American journal of ophthalmology.

[44]  Richard Wormald,et al.  The estimated prevalence and incidence of late stage age related macular degeneration in the UK , 2012, British Journal of Ophthalmology.

[45]  Action on AMD. Optimising patient management: act now to ensure current and continual delivery of best possible patient care , 2012, Eye.

[46]  B. Nan,et al.  Racial differences in age-related macular degeneration rates in the United States: a longitudinal analysis of a managed care network. , 2011, American journal of ophthalmology.

[47]  Sascha Fauser,et al.  Delay between medical indication to anti-VEGF treatment in age-related macular degeneration can result in a loss of visual acuity , 2011, Graefe's Archive for Clinical and Experimental Ophthalmology.

[48]  Richard F Spaide,et al.  Prevalence and significance of subretinal drusenoid deposits (reticular pseudodrusen) in age-related macular degeneration. , 2010, Ophthalmology.

[49]  Emily Y Chew,et al.  Summary results and recommendations from the age-related eye disease study. , 2009, Archives of ophthalmology.

[50]  J. Saaddine,et al.  Forecasting age-related macular degeneration through the year 2050: the potential impact of new treatments. , 2009, Archives of ophthalmology.

[51]  G Quentel,et al.  Prevalence of reticular pseudodrusen in age-related macular degeneration with newly diagnosed choroidal neovascularisation , 2006, British Journal of Ophthalmology.

[52]  Ronald Klein,et al.  A simplified severity scale for age-related macular degeneration: AREDS Report No. 18. , 2005, Archives of ophthalmology.

[53]  Johanna M Seddon,et al.  Risk factors for the incidence of Advanced Age-Related Macular Degeneration in the Age-Related Eye Disease Study (AREDS) AREDS report no. 19. , 2003, Ophthalmology.

[54]  N. Bressler,et al.  Relationship of drusen and abnormalities of the retinal pigment epithelium to the prognosis of neovascular macular degeneration. The Macular Photocoagulation Study Group. , 1990, Archives of ophthalmology.

[55]  U. Schmidt-Erfurth,et al.  HAWK and HARRIER: Phase 3, Multicenter, Randomized, Double-Masked Trials of Brolucizumab for Neovascular Age-Related Macular Degeneration. , 2019, Ophthalmology.

[56]  M. Mandai,et al.  Evaluation of the Surgical Device and Procedure for Extracellular Matrix-Scaffold-Supported Human iPSC-Derived Retinal Pigment Epithelium Cell Sheet Transplantation. , 2017, Investigative ophthalmology & visual science.

[57]  Sina Farsiu,et al.  Drusen Volume and Retinal Pigment Epithelium Abnormal Thinning Volume Predict 2-Year Progression of Age-Related Macular Degeneration. , 2016, Ophthalmology.

[58]  Geraint Rees,et al.  Automated analysis of retinal imaging using machine learning techniques for computer vision , 2016, F1000Research.

[59]  Joan W. Miller,et al.  Age-related macular degeneration. , 2008, The New England journal of medicine.

[60]  A. Ramé [Age-related macular degeneration]. , 2006, Revue de l'infirmiere.

[61]  Leslie Hyman,et al.  A Simplified Severity Scale for Age-Related Macular Degeneration , 2005 .

[62]  The Age-Related Eye Disease Study system for classifying age-related macular degeneration from stereoscopic color fundus photographs: the Age-Related Eye Disease Study Report Number 6. , 2001, American journal of ophthalmology.

[63]  Risk factors associated with age-related macular degeneration. A case-control study in the age-related eye disease study: Age-Related Eye Disease Study Report Number 3. , 2000, Ophthalmology.

[64]  R. Klein,et al.  Risk Factors Associated with Age-Related Macular Degeneration: A Case-control Study in the Age-Related Eye Disease Study: Age-Related Eye Disease Study , 2000 .

[65]  R. Klein,et al.  The five-year incidence and progression of age-related maculopathy: the Beaver Dam Eye Study. , 1997, Ophthalmology.