Clinically applicable deep learning for diagnosis and referral in retinal disease

Retinal Diseases

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

JAMA Ophthalmol

Wilson M, Chopra R, Wilson MZ, Cooper C, MacWilliams P, Liu Y, Wulczyn E, Florea D, Hughes CO, Karthikesalingam A, Khalid H, Vermeirsch S, Nicholson L, Keane PA, Balaskas K, Kelly CJ

doi: 10.1001/jamaophthalmol.2021.2273

Blindness and Vision Impairment

Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

JAMA Ophthalmol.

Brown JM, Campbell JP, Beers A, hang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF, for the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium

Cornea and Corneal DiseasesFurther Publications

Multi-Disease Deep-Learning Neural Network for the Diagnosis of Corneal Diseases

Am J Ophthalmol

Elsawy A, Eleiwa T, Chase C, Ozcan E, Tolba M, Feuer W, Abdel-Mottaleb M, Shousha MA

doi: 10.1016/j.ajo.2021.01.018

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

Panel-Based Clinical Genetic Testing in 85 Children with Inherited Retinal Disease

Ophthalmology.

Taylor RL, Parry NR, Barton SJ, Campbell C, Delaney CM, Ellingford JM, Hall G, Hardcastle C, Morarji J, Nichol EJ, Williams LC, Douzgou S, Clayton-Smith J, Ramsden SC, Sharma V, Biswas S, Lloyd IC, Ashworth JL, Black GC, Sergouniotis PI

Retinal Diseases

Clinically Focused Molecular Investigation of 1000 Consecutive Families with Inherited Retinal Disease

Ophthalmology.

Stone EM, Andorf JL, Whitmore SS, DeLuca AP, Giacalone JC, Streb LM, Braun TA, Mullins RF, Scheetz TE, Sheffield VC, Tucker BA

doi: 10.1016/j.ophtha.

Glaucoma and Ocular PressureFurther Publications

Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images

Am J Ophthalmol

Mehta P, Petersen CA, Wen JC, Banitt MR, Chen PP, Bojikian KD, Egan C, Lee SI, Balazinska M, Lee AY, Rokem A

doi: 10.1016/j.ajo.2021.04.021

Retinal Diseases

Fundus-controlled perimetry (microperimetry): Application as outcome measure in clinical trials

Prog Retin Eye Res

Pfau M, Jolly JK, Wu Z, Denniss J, Lad EM, Guymer RH, Fleckenstein M, Holz FG, Schmitz-Valckenberg S

doi: 10.1016/j.preteyeres.2020.100907

Macular Diseases

Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data

Acta Ophthalmol

Potapenko I, Kristensen M, Thiesson B, Ilginis T, Lykke Sørensen T, Nouri Hajari J, Fuchs J, Hamann S, la Cour M

doi: 10.1111/aos.14895

Related content

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

Multi-Disease Deep-Learning Neural Network for the Diagnosis of Corneal Diseases

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

Panel-Based Clinical Genetic Testing in 85 Children with Inherited Retinal Disease

Clinically Focused Molecular Investigation of 1000 Consecutive Families with Inherited Retinal Disease

Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images

Fundus-controlled perimetry (microperimetry): Application as outcome measure in clinical trials

Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data