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Cited by in F6Publishing
For: Zéboulon P, Debellemanière G, Bouvet M, Gatinel D. Corneal Topography Raw Data Classification Using a Convolutional Neural Network. Am J Ophthalmol 2020;219:33-9. [PMID: 32533948 DOI: 10.1016/j.ajo.2020.06.005] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Crahay FX, Debellemanière G, Tobalem S, Ghazal W, Moran S, Gatinel D. Quantitative interocular comparison of total corneal surface area and corneal diameter in patients with highly asymmetric keratoconus. Sci Rep 2022;12:4276. [PMID: 35277548 DOI: 10.1038/s41598-022-08021-6] [Reference Citation Analysis]
2 Kang L, Ballouz D, Woodward MA. Artificial intelligence and corneal diseases. Curr Opin Ophthalmol 2022. [PMID: 35819899 DOI: 10.1097/ICU.0000000000000885] [Reference Citation Analysis]
3 Shanthi S, Aruljyothi L, Balasundaram MB, Janakiraman A, Nirmaladevi K, Pyingkodi M. Artificial intelligence applications in different imaging modalities for corneal topography. Surv Ophthalmol 2021:S0039-6257(21)00176-4. [PMID: 34450134 DOI: 10.1016/j.survophthal.2021.08.004] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
4 Reddy JC, Bhamidipati P, Dwivedi S, Dhara KK, Joshi V, Hasnat Ali M, Vaddavalli PK. KEDOP: Keratoconus early detection of progression using tomography images. Eur J Ophthalmol 2022;:11206721221087566. [PMID: 35343267 DOI: 10.1177/11206721221087566] [Reference Citation Analysis]
5 Rampat R, Deshmukh R, Chen X, Ting DSW, Said DG, Dua HS, Ting DSJ. Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions. Asia Pac J Ophthalmol (Phila) 2021;10:268-81. [PMID: 34224467 DOI: 10.1097/APO.0000000000000394] [Reference Citation Analysis]
6 Jiménez-García M, Issarti I, Kreps EO, Ní Dhubhghaill S, Koppen C, Varssano D, Rozema JJ, On Behalf Of The Redcake Study Group. Forecasting Progressive Trends in Keratoconus by Means of a Time Delay Neural Network. J Clin Med 2021;10:3238. [PMID: 34362023 DOI: 10.3390/jcm10153238] [Reference Citation Analysis]
7 Santodomingo-Rubido J, Carracedo G, Suzaki A, Villa-Collar C, Vincent SJ, Wolffsohn JS. Keratoconus: An updated review. Cont Lens Anterior Eye 2022;:101559. [PMID: 34991971 DOI: 10.1016/j.clae.2021.101559] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [Reference Citation Analysis]
8 Al-timemy AH, Ghaeb NH, Mosa ZM, Escudero J. Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps. Cogn Comput. [DOI: 10.1007/s12559-021-09880-3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
9 Crahay FX, Debellemanière G, Tobalem S, Ghazal W, Moran S, Gatinel D. Quantitative comparison of corneal surface areas in keratoconus and normal eyes. Sci Rep 2021;11:6840. [PMID: 33767220 DOI: 10.1038/s41598-021-86185-3] [Reference Citation Analysis]
10 Chen X, Zhao J, Iselin KC, Borroni D, Romano D, Gokul A, McGhee CNJ, Zhao Y, Sedaghat MR, Momeni-Moghaddam H, Ziaei M, Kaye S, Romano V, Zheng Y. Keratoconus detection of changes using deep learning of colour-coded maps. BMJ Open Ophthalmol 2021;6:e000824. [PMID: 34337155 DOI: 10.1136/bmjophth-2021-000824] [Cited by in F6Publishing: 1] [Reference Citation Analysis]