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Items: 1 to 20 of 100

1.

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.

Shibata N, Tanito M, Mitsuhashi K, Fujino Y, Matsuura M, Murata H, Asaoka R.

Sci Rep. 2018 Oct 2;8(1):14665. doi: 10.1038/s41598-018-33013-w.

2.

A deep learning model for the detection of both advanced and early glaucoma using fundus photography.

Ahn JM, Kim S, Ahn KS, Cho SH, Lee KB, Kim US.

PLoS One. 2018 Nov 27;13(11):e0207982. doi: 10.1371/journal.pone.0207982. eCollection 2018. Erratum in: PLoS One. 2019 Jan 25;14(1):e0211579.

3.

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

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.

JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.

PMID:
27898976
4.

Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

Li Z, He Y, Keel S, Meng W, Chang RT, He M.

Ophthalmology. 2018 Aug;125(8):1199-1206. doi: 10.1016/j.ophtha.2018.01.023. Epub 2018 Mar 2.

PMID:
29506863
5.

Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning.

Bajwa MN, Malik MI, Siddiqui SA, Dengel A, Shafait F, Neumeier W, Ahmed S.

BMC Med Inform Decis Mak. 2019 Jul 17;19(1):136. doi: 10.1186/s12911-019-0842-8.

6.

Comparison of quantitative imaging devices and subjective optic nerve head assessment by general ophthalmologists to differentiate normal from glaucomatous eyes.

Vessani RM, Moritz R, Batis L, Zagui RB, Bernardoni S, Susanna R.

J Glaucoma. 2009 Mar;18(3):253-61. doi: 10.1097/IJG.0b013e31818153da.

PMID:
19295383
7.

Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images.

Son J, Shin JY, Kim HD, Jung KH, Park KH, Park SJ.

Ophthalmology. 2019 May 31. pii: S0161-6420(19)30374-4. doi: 10.1016/j.ophtha.2019.05.029. [Epub ahead of print]

8.

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs.

Christopher M, Belghith A, Bowd C, Proudfoot JA, Goldbaum MH, Weinreb RN, Girkin CA, Liebmann JM, Zangwill LM.

Sci Rep. 2018 Nov 12;8(1):16685. doi: 10.1038/s41598-018-35044-9.

9.

Screening Glaucoma With Red-free Fundus Photography Using Deep Learning Classifier and Polar Transformation.

Lee J, Kim Y, Kim JH, Park KH.

J Glaucoma. 2019 Mar;28(3):258-264. doi: 10.1097/IJG.0000000000001187.

PMID:
30676415
10.

Evaluation of a Deep Learning System for Identifying Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

Al-Aswad LA, Kapoor R, Chu CK, Walters S, Gong D, Garg A, Gopal K, Patel V, Sameer T, Rogers TW, Nicolas J, De Moraes CG, Moazami G.

J Glaucoma. 2019 Jun 21. doi: 10.1097/IJG.0000000000001319. [Epub ahead of print]

PMID:
31233461
11.

ASSESSMENT OF CENTRAL SEROUS CHORIORETINOPATHY DEPICTED ON COLOR FUNDUS PHOTOGRAPHS USING DEEP LEARNING.

Zhen Y, Chen H, Zhang X, Meng X, Zhang J, Pu J.

Retina. 2019 Jul 3. doi: 10.1097/IAE.0000000000002621. [Epub ahead of print]

PMID:
31283737
12.

Development of a resident training module for systematic optic disc evaluation in glaucoma.

Law SK, Tamboli DA, Ou Y, Giaconi JA, Caprioli J.

J Glaucoma. 2012 Dec;21(9):601-7. doi: 10.1097/IJG.0b013e31821db3c7.

PMID:
21602706
13.

A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography.

Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B, Peters A, Heid IM, Palm C, Weber BHF.

Ophthalmology. 2018 Sep;125(9):1410-1420. doi: 10.1016/j.ophtha.2018.02.037. Epub 2018 Apr 10.

14.

Detection of structural damage from glaucoma with confocal laser image analysis.

Uchida H, Brigatti L, Caprioli J.

Invest Ophthalmol Vis Sci. 1996 Nov;37(12):2393-401.

PMID:
8933756
15.

Deep learning applications in ophthalmology.

Rahimy E.

Curr Opin Ophthalmol. 2018 May;29(3):254-260. doi: 10.1097/ICU.0000000000000470. Review.

PMID:
29528860
16.

Glaucoma diagnostics.

Geimer SA.

Acta Ophthalmol. 2013 Feb;91 Thesis 1:1-32. doi: 10.1111/aos.12072.

17.

Automated Identification of Diabetic Retinopathy Using Deep Learning.

Gargeya R, Leng T.

Ophthalmology. 2017 Jul;124(7):962-969. doi: 10.1016/j.ophtha.2017.02.008. Epub 2017 Mar 27.

PMID:
28359545
18.

Macular segmentation with optical coherence tomography.

Ishikawa H, Stein DM, Wollstein G, Beaton S, Fujimoto JG, Schuman JS.

Invest Ophthalmol Vis Sci. 2005 Jun;46(6):2012-7.

19.

Accuracy of GDx VCC, HRT I, and clinical assessment of stereoscopic optic nerve head photographs for diagnosing glaucoma.

Reus NJ, de Graaf M, Lemij HG.

Br J Ophthalmol. 2007 Mar;91(3):313-8. Epub 2006 Oct 11.

20.

Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features.

Abràmoff MD, Alward WL, Greenlee EC, Shuba L, Kim CY, Fingert JH, Kwon YH.

Invest Ophthalmol Vis Sci. 2007 Apr;48(4):1665-73.

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