This paper is in the following e-collection/theme issue:

Digital Health Reviews (1316) Machine Learning (1488) Imaging Informatics (198) Ophthalmology (47) Artificial Intelligence (1407) Computer-Aided Diagnosis (133)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27414, first published .
Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Authors of this article:

Ali Q Saeed1, 2 Author Orcid Image ;   Siti Norul Huda Sheikh Abdullah1 Author Orcid Image ;   Jemaima Che-Hamzah3 Author Orcid Image ;   Ahmad Tarmizi Abdul Ghani1 Author Orcid Image
Article Authors Cited by (21) Tweetations (2) Metrics

Journals

  1. Wang R, Zuo G, Li K, Li W, Xuan Z, Han Y, Yang W. Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy. Frontiers in Endocrinology 2022;13 View
  2. Camara J, Neto A, Pires I, Villasana M, Zdravevski E, Cunha A. Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification. Journal of Imaging 2022;8(2):19 View
  3. Ali H, Shah Z. Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review. JMIR Medical Informatics 2022;10(6):e37365 View
  4. Zhao J, Lu Y, Zhu S, Li K, Jiang Q, Yang W. Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis. Frontiers in Pharmacology 2022;13 View
  5. Chang C, Chang C, Lin Y, Su W, Chen H. A Glaucoma Detection System Based on Generative Adversarial Network and Incremental Learning. Applied Sciences 2023;13(4):2195 View
  6. Saeed A, Sheikh Abdullah S, Che-Hamzah J, Abdul Ghani A, Abu-ain W. Synthesizing Retinal Images using End-To-End VAEs-GAN Pipeline-Based Sharpening and Varying Layer. Multimedia Tools and Applications 2024;83(1):1283 View
  7. Zedan M, Zulkifley M, Ibrahim A, Moubark A, Kamari N, Abdani S. Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review. Diagnostics 2023;13(13):2180 View
  8. Xu Y, Zheng X, Li Y, Ye X, Cheng H, Wang H, Lyu J. Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review. Journal of Evidence-Based Medicine 2023;16(3):342 View
  9. Zhou Z, Zhang X, Tang X, Grzybowski A, Ye J, Lou L. Global research of artificial intelligence in strabismus: a bibliometric analysis. Frontiers in Medicine 2023;10 View
  10. Zhang X. Global research on artificial intelligence in thyroid-associated ophthalmopathy: A bibliometric analysis. Advances in Ophthalmology Practice and Research 2024;4(1):1 View
  11. D. N. S, Pai R, Bhat S, Pai M. M. M. Deep learning-based automated spine fracture type identification with Clinically validated GAN generated CT images. Cogent Engineering 2024;11(1) View
  12. Waisberg E, Ong J, Kamran S, Masalkhi M, Paladugu P, Zaman N, Lee A, Tavakkoli A. Generative artificial intelligence in ophthalmology. Survey of Ophthalmology 2025;70(1):1 View
  13. Akpinar M, Sengur A, Faust O, Tong L, Molinari F, Acharya U. Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013–2023). Computer Methods and Programs in Biomedicine 2024;254:108253 View
  14. Chaurasia A, MacGregor S, Craig J, Mackey D, Hewitt A. Assessing the Efficacy of Synthetic Optic Disc Images for Detecting Glaucomatous Optic Neuropathy Using Deep Learning. Translational Vision Science & Technology 2024;13(6):1 View
  15. Zhang X, Zhou Z, Cai Y, Grzybowski A, Ye J, Lou L. Global research of artificial intelligence in eyelid diseases: A bibliometric analysis. Heliyon 2024;10(14):e34979 View
  16. Anwer A, Karacan H, Enver L, Cabuk G. Machine learning applications for vascular stenosis detection in computed tomography angiography: a systematic review and meta-analysis. Neural Computing and Applications 2024;36(29):17767 View
  17. Christopher M, Hallaj S, Jiravarnsirikul A, Baxter S, Zangwill L. Novel Technologies in Artificial Intelligence and Telemedicine for Glaucoma Screening. Journal of Glaucoma 2024;33(8S):S26 View
  18. Tonti E, Tonti S, Mancini F, Bonini C, Spadea L, D’Esposito F, Gagliano C, Musa M, Zeppieri M. Artificial Intelligence and Advanced Technology in Glaucoma: A Review. Journal of Personalized Medicine 2024;14(10):1062 View

Books/Policy Documents

  1. Bellapukonda P, Ayyadurai S, Mirza M, Subramaniam S. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). View