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For: Christopher M, Bowd C, Belghith A, Goldbaum MH, Weinreb RN, Fazio MA, Girkin CA, Liebmann JM, Zangwill LM. Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps. Ophthalmology 2020;127:346-56. [PMID: 31718841 DOI: 10.1016/j.ophtha.2019.09.036] [Cited by in Crossref: 35] [Cited by in F6Publishing: 27] [Article Influence: 11.7] [Reference Citation Analysis]
Number Citing Articles
1 Christopher M, Bowd C, Proudfoot JA, Belghith A, Goldbaum MH, Rezapour J, Fazio MA, Girkin CA, De Moraes G, Liebmann JM, Weinreb RN, Zangwill LM. Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT. Ophthalmology 2021:S0161-6420(21)00316-X. [PMID: 33901527 DOI: 10.1016/j.ophtha.2021.04.022] [Reference Citation Analysis]
2 Swanson WH, King BJ, Burns SA. Interpreting Retinal Nerve Fiber Layer Reflectance Defects Based on Presence of Retinal Nerve Fiber Bundles. Optom Vis Sci 2021;98:531-41. [PMID: 33973913 DOI: 10.1097/OPX.0000000000001690] [Reference Citation Analysis]
3 Buisson M, Navel V, Labbé A, Watson SL, Baker JS, Murtagh P, Chiambaretta F, Dutheil F. Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta-analysis. Clin Exp Ophthalmol 2021. [PMID: 34506041 DOI: 10.1111/ceo.14000] [Reference Citation Analysis]
4 Lazaridis G, Montesano G, Afgeh SS, Mohamed-Noriega J, Ourselin S, Lorenzi M, Garway-Heath DF. Predicting visual fields from optical coherence tomography via an ensemble of deep representation learners. Am J Ophthalmol 2022:S0002-9394(21)00663-2. [PMID: 34998718 DOI: 10.1016/j.ajo.2021.12.020] [Reference Citation Analysis]
5 Yu HH, Maetschke SR, Antony BJ, Ishikawa H, Wollstein G, Schuman JS, Garnavi R. Estimating Global Visual Field Indices in Glaucoma by Combining Macula and Optic Disc OCT Scans Using 3-Dimensional Convolutional Neural Networks. Ophthalmol Glaucoma 2021;4:102-12. [PMID: 32826205 DOI: 10.1016/j.ogla.2020.07.002] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
6 Huang X, Sun J, Majoor J, Vermeer KA, Lemij H, Elze T, Wang M, Boland MV, Pasquale LR, Mohammadzadeh V, Nouri-Mahdavi K, Johnson C, Yousefi S. Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence. Transl Vis Sci Technol 2021;10:16. [PMID: 34398225 DOI: 10.1167/tvst.10.9.16] [Reference Citation Analysis]
7 Shin J, Kim S, Kim J, Park K. Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices. Transl Vis Sci Technol 2021;10:4. [PMID: 34086043 DOI: 10.1167/tvst.10.7.4] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Arsalan M, Baek NR, Owais M, Mahmood T, Park KR. Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa. Sensors (Basel) 2020;20:E3454. [PMID: 32570943 DOI: 10.3390/s20123454] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
9 Schuman JS, Angeles Ramos Cadena ML, McGee R, Al-Aswad LA, Medeiros FA; Collaborative Community for Ophthalmic Imaging Executive Committee and Glaucoma Workgroup. A Case for The Use of Artificial Intelligence in Glaucoma Assessment. Ophthalmol Glaucoma 2021:S2589-4196(21)00280-5. [PMID: 34954220 DOI: 10.1016/j.ogla.2021.12.003] [Reference Citation Analysis]
10 He M, Li Z, Liu C, Shi D, Tan Z. Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge. Asia Pac J Ophthalmol (Phila) 2020;9:299-307. [PMID: 32694344 DOI: 10.1097/APO.0000000000000301] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 7.0] [Reference Citation Analysis]
11 Hashimoto Y, Kiwaki T, Sugiura H, Asano S, Murata H, Fujino Y, Matsuura M, Miki A, Mori K, Ikeda Y, Kanamoto T, Yamagami J, Inoue K, Tanito M, Yamanishi K, Asaoka R. Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field. Transl Vis Sci Technol 2021;10:28. [PMID: 34812893 DOI: 10.1167/tvst.10.13.28] [Reference Citation Analysis]
12 Girard MJA, Schmetterer L. Artificial intelligence and deep learning in glaucoma: Current state and future prospects. Prog Brain Res 2020;257:37-64. [PMID: 32988472 DOI: 10.1016/bs.pbr.2020.07.002] [Reference Citation Analysis]
13 Lavric A, Popa V, Takahashi H, Hazarbassanov RM, Yousefi S. Association between visual field damage and corneal structural parameters. Sci Rep 2021;11:10732. [PMID: 34031496 DOI: 10.1038/s41598-021-90298-0] [Reference Citation Analysis]
14 Shin Y, Cho H, Jeong HC, Seong M, Choi JW, Lee WJ. Deep Learning-based Diagnosis of Glaucoma Using Wide-field Optical Coherence Tomography Images. J Glaucoma 2021;30:803-12. [PMID: 33979115 DOI: 10.1097/IJG.0000000000001885] [Reference Citation Analysis]
15 Xu L, Asaoka R, Kiwaki T, Murata H, Fujino Y, Matsuura M, Hashimoto Y, Asano S, Miki A, Mori K, Ikeda Y, Kanamoto T, Yamagami J, Inoue K, Tanito M, Yamanishi K. Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression. American Journal of Ophthalmology 2020;218:304-13. [DOI: 10.1016/j.ajo.2020.04.037] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
16 Christopher M, Bowd C, Belghith A, Goldbaum MH, Weinreb RN, Fazio MA, Girkin CA, Liebmann JM, Zangwill LM. Reply. Ophthalmology 2021:S0161-6420(21)00642-4. [PMID: 34629192 DOI: 10.1016/j.ophtha.2021.08.022] [Reference Citation Analysis]
17 Oh S, Park Y, Cho KJ, Kim SJ. Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation. Diagnostics (Basel) 2021;11:510. [PMID: 33805685 DOI: 10.3390/diagnostics11030510] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
18 Wong SH, Tsai JC. Telehealth and Screening Strategies in the Diagnosis and Management of Glaucoma. J Clin Med 2021;10:3452. [PMID: 34441748 DOI: 10.3390/jcm10163452] [Reference Citation Analysis]
19 Bowd C, Belghith A, Christopher M, Goldbaum MH, Fazio MA, Girkin CA, Liebmann JM, de Moraes CG, Weinreb RN, Zangwill LM. Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest. Transl Vis Sci Technol 2021;10:19. [PMID: 34293095 DOI: 10.1167/tvst.10.8.19] [Reference Citation Analysis]
20 Camara J, Neto A, Pires IM, Villasana MV, Zdravevski E, Cunha A. A Comprehensive Review of Methods and Equipment for Aiding Automatic Glaucoma Tracking. Diagnostics 2022;12:935. [DOI: 10.3390/diagnostics12040935] [Reference Citation Analysis]
21 Diener R, Treder M, Eter N. [Diagnostics of diseases of the optic nerve head in times of artificial intelligence and big data]. Ophthalmologe 2021;118:893-9. [PMID: 33890129 DOI: 10.1007/s00347-021-01385-6] [Reference Citation Analysis]
22 Christopher M, Nakahara K, Bowd C, Proudfoot JA, Belghith A, Goldbaum MH, Rezapour J, Weinreb RN, Fazio MA, Girkin CA, Liebmann JM, De Moraes G, Murata H, Tokumo K, Shibata N, Fujino Y, Matsuura M, Kiuchi Y, Tanito M, Asaoka R, Zangwill LM. Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms. Transl Vis Sci Technol 2020;9:27. [PMID: 32818088 DOI: 10.1167/tvst.9.2.27] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
23 Datta S, Mariottoni EB, Dov D, Jammal AA, Carin L, Medeiros FA. RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure. Sci Rep 2021;11:12562. [PMID: 34131181 DOI: 10.1038/s41598-021-91493-9] [Reference Citation Analysis]
24 Mariottoni EB, Datta S, Dov D, Jammal AA, Berchuck SI, Tavares IM, Carin L, Medeiros FA. Artificial Intelligence Mapping of Structure to Function in Glaucoma. Transl Vis Sci Technol 2020;9:19. [PMID: 32818080 DOI: 10.1167/tvst.9.2.19] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 5.5] [Reference Citation Analysis]
25 Wang M, Shen LQ, Pasquale LR, Wang H, Li D, Choi EY, Yousefi S, Bex PJ, Elze T. An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma. Transl Vis Sci Technol 2020;9:41. [PMID: 32908804 DOI: 10.1167/tvst.9.9.41] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
26 Kihara Y, Montesano G, Chen A, Amerasinghe N, Dimitriou C, Jacob A, Chabi A, Crabb DP, Lee AY. Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and Optical Coherence Tomography Imaging. Ophthalmology 2022:S0161-6420(22)00156-7. [PMID: 35202616 DOI: 10.1016/j.ophtha.2022.02.017] [Reference Citation Analysis]
27 Salazar H, Misra V, Swaminathan SS. Artificial intelligence and complex statistical modeling in glaucoma diagnosis and management. Curr Opin Ophthalmol 2021;32:105-17. [PMID: 33395111 DOI: 10.1097/ICU.0000000000000741] [Reference Citation Analysis]
28 Li JO, Liu H, Ting DS, Jeon S, Chan RP, Kim JE, Sim DA, Thomas PB, Lin H, Chen Y, Sakomoto T, Loewenstein A, Lam DS, Pasquale LR, Wong TY, Lam LA, Ting DS. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Progress in Retinal and Eye Research 2021;82:100900. [DOI: 10.1016/j.preteyeres.2020.100900] [Cited by in Crossref: 17] [Cited by in F6Publishing: 16] [Article Influence: 17.0] [Reference Citation Analysis]
29 Zhang Y, Wang N, Liu H. Re: Christopher et al.: Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head en face images and retinal nerve fiber layer thickness maps (Ophthalmology. 2020;127:346-356). Ophthalmology 2022;129:e4-5. [PMID: 34629193 DOI: 10.1016/j.ophtha.2021.07.035] [Reference Citation Analysis]
30 Park K, Kim J, Lee J. A deep learning approach to predict visual field using optical coherence tomography. PLoS One 2020;15:e0234902. [PMID: 32628672 DOI: 10.1371/journal.pone.0234902] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
31 Asano S, Asaoka R, Murata H, Hashimoto Y, Miki A, Mori K, Ikeda Y, Kanamoto T, Yamagami J, Inoue K. Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images. Sci Rep 2021;11:2214. [PMID: 33500462 DOI: 10.1038/s41598-020-79494-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
32 von der Emde L, Pfau M, Holz FG, Fleckenstein M, Kortuem K, Keane PA, Rubin DL, Schmitz-Valckenberg S. AI-based structure-function correlation in age-related macular degeneration. Eye (Lond) 2021;35:2110-8. [PMID: 33767409 DOI: 10.1038/s41433-021-01503-3] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Soltanian-Zadeh S, Kurokawa K, Liu Z, Zhang F, Saeedi O, Hammer DX, Miller DT, Farsiu S. Weakly supervised individual ganglion cell segmentation from adaptive optics OCT images for glaucomatous damage assessment. Optica 2021;8:642-51. [PMID: 35174258 DOI: 10.1364/optica.418274] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
34 Díaz-Alemán VT, Fumero Batista FJ, Alayón Miranda S, Ángel-Pereira D, Arteaga-Hernández VJ, Sigut Saavedra JF. Ganglion cell layer analysis with deep learning in glaucoma diagnosis. Arch Soc Esp Oftalmol (Engl Ed) 2021;96:181-8. [PMID: 33279356 DOI: 10.1016/j.oftal.2020.09.010] [Reference Citation Analysis]
35 Hashimoto Y, Asaoka R, Kiwaki T, Sugiura H, Asano S, Murata H, Fujino Y, Matsuura M, Miki A, Mori K, Ikeda Y, Kanamoto T, Yamagami J, Inoue K, Tanito M, Yamanishi K. Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma. Br J Ophthalmol 2021;105:507-13. [PMID: 32593978 DOI: 10.1136/bjophthalmol-2019-315600] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
36 Sułot D, Alonso-Caneiro D, Ksieniewicz P, Krzyzanowska-Berkowska P, Iskander DR. Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method. PLoS One 2021;16:e0252339. [PMID: 34086716 DOI: 10.1371/journal.pone.0252339] [Reference Citation Analysis]