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For: Mirzania D, Thompson AC, Muir KW. Applications of deep learning in detection of glaucoma: A systematic review. Eur J Ophthalmol 2021;31:1618-42. [PMID: 33274641 DOI: 10.1177/1120672120977346] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
Number Citing Articles
1 Abbas Q, Qureshi I, Yan J, Shaheed K. Machine Learning Methods for Diagnosis of Eye-Related Diseases: A Systematic Review Study Based on Ophthalmic Imaging Modalities. Arch Computat Methods Eng. [DOI: 10.1007/s11831-022-09720-z] [Reference Citation Analysis]
2 Kanar HS, Toz HT, Penbe A. Comparison of retinal nerve fiber layer, macular ganglion cell complex and choroidal thickness in patients with migraine with and without aura by using optical coherence tomography. Photodiagnosis Photodyn Ther 2021;34:102323. [PMID: 33962058 DOI: 10.1016/j.pdpdt.2021.102323] [Reference Citation Analysis]
3 Singh LK, Pooja, Garg H, Khanna M. Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images. Multimed Tools Appl. [DOI: 10.1007/s11042-022-12826-y] [Reference Citation Analysis]
4 Ran A, Cheung CY. Re: Xiong et al.: Multimodal machine learning using visual fields and peripapillary circular OCT scans in detection of glaucomatous optic neuropathy (Ophthalmology. 2021 Jul 30;S0161-6420(21)00565-0. doi: 10.1016/j.ophtha.2021.07.032. Online ahead of print.). Ophthalmology 2022. [DOI: 10.1016/j.ophtha.2021.12.009] [Reference Citation Analysis]
5 Li Y, Foo LL, Wong CW, Li J, Hoang QV, Schmetterer L, Ting DSW, Ang M. Pathologic myopia: advances in imaging and the potential role of artificial intelligence. Br J Ophthalmol 2022:bjophthalmol-2021-320926. [PMID: 35288438 DOI: 10.1136/bjophthalmol-2021-320926] [Reference Citation Analysis]
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