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Journal Abstract Search

3865 related items for PubMed ID: 27898976

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  • 2. Automated analysis of retinal images for detection of referable diabetic retinopathy.
    Abràmoff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR, Massin P, Cochener B, Gain P, Tang L, Lamard M, Moga DC, Quellec G, Niemeijer M.
    JAMA Ophthalmol; 2013 Mar; 131(3):351-7. PubMed ID: 23494039
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  • 6. An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs.
    Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, Lee PY, Shaw J, Ting D, Wong TY, Taylor H, Chang R, He M.
    Diabetes Care; 2018 Dec; 41(12):2509-2516. PubMed ID: 30275284
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  • 7. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.
    Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M.
    Invest Ophthalmol Vis Sci; 2016 Oct 01; 57(13):5200-5206. PubMed ID: 27701631
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  • 8. Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy - Artificial intelligence versus clinician for screening.
    Shah P, Mishra DK, Shanmugam MP, Doshi B, Jayaraj H, Ramanjulu R.
    Indian J Ophthalmol; 2020 Feb 01; 68(2):398-405. PubMed ID: 31957737
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  • 9. 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 01; 125(8):1199-1206. PubMed ID: 29506863
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  • 10. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.
    Bellemo V, Lim ZW, Lim G, Nguyen QD, Xie Y, Yip MYT, Hamzah H, Ho J, Lee XQ, Hsu W, Lee ML, Musonda L, Chandran M, Chipalo-Mutati G, Muma M, Tan GSW, Sivaprasad S, Menon G, Wong TY, Ting DSW.
    Lancet Digit Health; 2019 May 01; 1(1):e35-e44. PubMed ID: 33323239
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  • 11. Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs.
    Keel S, Li Z, Scheetz J, Robman L, Phung J, Makeyeva G, Aung K, Liu C, Yan X, Meng W, Guymer R, Chang R, He M.
    Clin Exp Ophthalmol; 2019 Nov 01; 47(8):1009-1018. PubMed ID: 31215760
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  • 13. Diabetic retinopathy screening using deep neural network.
    Ramachandran N, Hong SC, Sime MJ, Wilson GA.
    Clin Exp Ophthalmol; 2018 May 01; 46(4):412-416. PubMed ID: 28881490
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  • 15. Deep Learning Detection of Sea Fan Neovascularization From Ultra-Widefield Color Fundus Photographs of Patients With Sickle Cell Hemoglobinopathy.
    Cai S, Parker F, Urias MG, Goldberg MF, Hager GD, Scott AW.
    JAMA Ophthalmol; 2021 Feb 01; 139(2):206-213. PubMed ID: 33377944
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  • 16. Detection of Diabetic Retinopathy from Ultra-Widefield Scanning Laser Ophthalmoscope Images: A Multicenter Deep Learning Analysis.
    Tang F, Luenam P, Ran AR, Quadeer AA, Raman R, Sen P, Khan R, Giridhar A, Haridas S, Iglicki M, Zur D, Loewenstein A, Negri HP, Szeto S, Lam BKY, Tham CC, Sivaprasad S, Mckay M, Cheung CY.
    Ophthalmol Retina; 2021 Nov 01; 5(11):1097-1106. PubMed ID: 33540169
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