Predicting Risk of Developing Diabetic Retinopathy using Deep Learning

[1]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[2]  M. Schneck,et al.  A multifocal electroretinogram model predicting the development of diabetic retinopathy , 2006, Progress in Retinal and Eye Research.

[3]  Michael V. McConnell,et al.  Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning , 2017, Nature Biomedical Engineering.

[4]  P. Scanlon.  MICROVASCULAR COMPLICATIONS — RETINOPATHY ( JK SUN AND PS SILVA , 2017 .

[5]  N. Wald,et al.  Prevention of blindness by screening for diabetic retinopathy: a quantitative assessment. , 1989, BMJ.

[6]  M. Abràmoff,et al.  Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. , 2016, Investigative ophthalmology & visual science.

[7]  Jennifer I. Lim,et al.  Diabetic Retinopathy Preferred Practice Pattern®. , 2019, Ophthalmology.

[8]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  E. Stefánsson,et al.  Diabetic eye screening with variable screening intervals based on individual risk factors is safe and effective in ophthalmic practice , 2020, Acta ophthalmologica.

[10]  S. Sivaprasad,et al.  Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone. , 2019, JAMA ophthalmology.

[11]  M. Blumenkranz,et al.  The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography. , 2002, American journal of ophthalmology.

[12]  A. Keech,et al.  Biomarkers in Diabetic Retinopathy. , 2015, The review of diabetic studies : RDS.

[13]  Subhashini Venugopalan,et al.  Detection of anaemia from retinal fundus images via deep learning , 2019, Nature Biomedical Engineering.

[14]  April Y. Maa,et al.  Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs. , 2018, Ophthalmology.

[15]  J. Cunha-Vaz,et al.  Diabetic Retinopathy Phenotypes of Progression to Macular Edema: Pooled Analysis From Independent Longitudinal Studies of up to 2 Years' Duration. , 2017, Investigative ophthalmology & visual science.

[16]  11. Microvascular Complications and Foot Care: Standards of Medical Care in Diabetes−2020 , 2019, Diabetes Care.

[17]  Felipe A. Medeiros,et al.  From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs , 2018, Ophthalmology.

[18]  J. Shaw,et al.  IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. , 2011, Diabetes research and clinical practice.

[19]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[20]  Tien Yin Wong,et al.  Diabetic retinopathy , 2010, The Lancet.

[21]  H. Zisser,et al.  A Virtual Type 2 Diabetes Clinic Using Continuous Glucose Monitoring and Endocrinology Visits , 2019, Journal of diabetes science and technology.

[22]  F. Arcadu,et al.  Deep learning algorithm predicts diabetic retinopathy progression in individual patients. , 2019, NPJ digital medicine.

[23]  Jennifer K. Sun,et al.  Diabetic Retinopathy: A Position Statement by the American Diabetes Association , 2017, Diabetes Care.

[24]  Rishi P. Singh,et al.  The role of anti-vascular endothelial growth factor (anti-VEGF) in the management of proliferative diabetic retinopathy , 2018, Drugs in context.

[25]  N. Razavian,et al.  Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.

[26]  Grading Diabetic Retinopathy from Stereoscopic Color Fundus Photographs - An Extension of the Modified Airlie House Classification: ETDRS Report Number 10. , 2020, Ophthalmology.

[27]  M. Abràmoff,et al.  Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices , 2018, npj Digital Medicine.

[28]  J. Shaw,et al.  Retinal Arteriolar Caliber Predicts Incident Retinopathy , 2008, Diabetes Care.

[29]  Matthew D. Davis,et al.  Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. , 2003, Ophthalmology.

[30]  Janelle Downing,et al.  Use of a Connected Glucose Meter and Certified Diabetes Educator Coaching to Decrease the Likelihood of Abnormal Blood Glucose Excursions: The Livongo for Diabetes Program , 2017, Journal of medical Internet research.

[31]  L. Aiello,et al.  Retinopathy in diabetes. , 2004, Diabetes care.

[32]  B. Modjtahedi,et al.  Two-Year Incidence of Retinal Intervention in Patients With Minimal or No Diabetic Retinopathy on Telemedicine Screening , 2019, JAMA ophthalmology.

[33]  Grading diabetic retinopathy from stereoscopic color fundus photographs--an extension of the modified Airlie House classification. ETDRS report number 10. Early Treatment Diabetic Retinopathy Study Research Group. , 1991, Ophthalmology.

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

[35]  M. Goldbaum,et al.  Predicting glaucoma prior to its onset using deep learning , 2019 .

[36]  R. T. Smith,et al.  Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD , 2020, Translational vision science & technology.

[37]  David S. Melnick,et al.  International evaluation of an AI system for breast cancer screening , 2020, Nature.

[38]  E. Finkelstein,et al.  Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes , 2017, JAMA.

[39]  D. Ting,et al.  Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review , 2016, Clinical & experimental ophthalmology.

[40]  Bilson J. L. Campana,et al.  Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program , 2019, npj Digital Medicine.

[41]  Mukund Sundararajan,et al.  Attribution in Scale and Space , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  M. Bachmann,et al.  Incidence and Progression of Diabetic Retinopathy During 17 Years of a Population-Based Screening Program in England , 2012, Diabetes Care.

[43]  Subhashini Venugopalan,et al.  Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning , 2018, Nature Communications.

[44]  Stephen J. Aldington,et al.  UKPDS 50: Risk factors for incidence and progression of retinopathy in Type II diabetes over 6 years from diagnosis , 2001, Diabetologia.

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

[46]  D. Hassabis,et al.  Predicting conversion to wet age-related macular degeneration using deep learning , 2020, Nature Medicine.

[47]  F. Ferris,et al.  How effective are treatments for diabetic retinopathy? , 1993, JAMA.

[48]  Ankur Taly,et al.  Axiomatic Attribution for Deep Networks , 2017, ICML.