Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence

Macular DiseasesRetinal Diseases

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

JAMA.

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR

doi: 10.1001/jama.2016.17216.

Retinal Diseases

Diabetic Retinopathy Screening Using a Gold Nanoparticle-Based Paper Strip Assay for the At-Home Detection of the Urinary Biomarker 8-Hydroxy-2'-deoxyguanosine (8-OHdG)

Am J Ophthalmol.

Hainsworth DP, Gangula A, Ghoshdastidar S, Kannan R, Upendran A

doi: 10.1016/j.ajo.2020.01.032.

Retinal Diseases

Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System

Acta Ophthalmol.

van der Heijden AA, Abramoff MD, Verbraak F, van Hecke MV, Liem A, Nijpels G

doi: 10.1111/aos.13613.

COMMENTED

Retinal Diseases

Comparing diabetic retinopathy lesions in scanning laser ophthalmoscopy and colour fundus photography

Acta Ophthalmol.

Nghiem AZ, Nderitu P, Lukic M, Khatun M, Largan R, Kortuem K, Balaskas K, Sim D

doi: 10.1111/aos.14106.

Macular Diseases

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks

JAMA Ophthalmol.

Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM

doi: 10.1001/

Retinal Diseases

Visual Field Loss in Patients With Diabetes in the Absence of Clinically-Detectable Vascular Retinopathy in a Nationally Representative Survey

Invest Ophthalmol Vis Sci.

Bao YK, Yan Y, Gordon M, McGill JB, Kass M, Rajagopal R

doi: 10.1167/iovs.19-28063.

Macular DiseasesRetinal Diseases

A Revised Approach for the Detection of Sight-Threatening Diabetic Macular Edema.

JAMA Ophthalmol.

Litvin TV, Bresnick GH, Cuadros JA, Selvin S, Kanai K, Ozawa GY

doi: 10.1001/

Retinal Diseases

Hemorrhage and/or Microaneurysm Severity and Count in Ultrawide Field Images and Early Treatment Diabetic Retinopathy Study Photography

Ophthalmology.

Silva PS, El-Rami H, Barham R, Gupta A, Fleming A, van Hemert J, Cavallerano JD, Sun JK, Aiello LP

doi: 10.1016/j.ophtha.2017.02.012.

Blindness and Vision ImpairmentRetinal Diseases

Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records

J Clin Med

Wang R, Miao Z, Liu T, Liu M, Grdinovac K, Song X, Liang Y, Delen D, Paiva W

doi: 10.3390/jcm10071473

Related content

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Diabetic Retinopathy Screening Using a Gold Nanoparticle-Based Paper Strip Assay for the At-Home Detection of the Urinary Biomarker 8-Hydroxy-2'-deoxyguanosine (8-OHdG)

Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System

Comparing diabetic retinopathy lesions in scanning laser ophthalmoscopy and colour fundus photography

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks

Visual Field Loss in Patients With Diabetes in the Absence of Clinically-Detectable Vascular Retinopathy in a Nationally Representative Survey

A Revised Approach for the Detection of Sight-Threatening Diabetic Macular Edema.

Hemorrhage and/or Microaneurysm Severity and Count in Ultrawide Field Images and Early Treatment Diabetic Retinopathy Study Photography

Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records