A multi-label transformer-based deep learning approach to predict focal visual field progression

Chen, Ling; Tseng, Vincent S.; Tsung, Ta-Hsin; Lu, Da-Wen

doi:10.1007/s00417-024-06393-1

Graefes Arch Clin Exp Ophthalmol

2024

DOI: 10.1007/s00417-024-06393-1

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A multi-label transformer-based deep learning approach to predict focal visual field progression

Ling Chen,

Vincent S. Tseng,

Ta-Hsin Tsung

et al.

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Application of artificial intelligence in glaucoma care: An updated review

Wu,

Lin,

Moghimi

2024

Taiwan Journal of Ophthalmology

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The application of artificial intelligence (AI) in ophthalmology has been increasingly explored in the past decade. Numerous studies have shown promising results supporting the utility of AI to improve the management of ophthalmic diseases, and glaucoma is of no exception. Glaucoma is an irreversible vision condition with insidious onset, complex pathophysiology, and chronic treatment. Since there remain various challenges in the clinical management of glaucoma, the potential role of AI in facilitating glaucoma care has garnered significant attention. In this study, we reviewed the relevant literature published in recent years that investigated the application of AI in glaucoma management. The main aspects of AI applications that will be discussed include glaucoma risk prediction, glaucoma detection and diagnosis, visual field estimation and pattern analysis, glaucoma progression detection, and other applications.

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Application of artificial intelligence in glaucoma care: An updated review

Wu,

Lin,

Moghimi

2024

Taiwan Journal of Ophthalmology

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show abstract

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A multi-label transformer-based deep learning approach to predict focal visual field progression

Cited by 1 publication

References 33 publications

Application of artificial intelligence in glaucoma care: An updated review

Application of artificial intelligence in glaucoma care: An updated review

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