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Glaucoma is a group of optic neuropathies characterized by progressive degeneration of the optic nerve with loss of retinal ganglion cells, resulting in irreversible visual field loss. Currently there is no cure for glaucoma, but successful intraocular pressure control can halt or significantly slow the progression of glaucoma, most effectively if the disease is detected at an early stage. Recent advances in artificial intelligence (AI), especially the advent of deep learning, have shown transformative impact on the healthcare industry. AI has shown potential roles in glaucoma, such as detection of signs of glaucomatous damage, assistance in the clinical diagnosis, and evaluation of disease prognosis. In this chapter, we will first provide an overview of the application of AI in glaucoma with a focus on the deep learning models, and then discuss the clinical and technical challenges of current AI systems. Although the challenges are undeniable, further research will likely accelerate emergence of effective AI based systems for glaucoma in clinical practice.
|Title of host publication||Artificial intelligence and ophthalmology|
|Subtitle of host publication||perks, perils and pitfalls|
|Editors||Parul Ichhpujani, Sahil Thakur|
|Place of Publication||Singapore|
|Publisher||Springer, Springer Nature|
|Number of pages||15|
|Publication status||Published - 23 Apr 2021|
|Name||Current Practices in Ophthalmology|
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- 1 Finished
Tracking Glaucoma Progression using Deep Learning Methods
1/01/19 → 31/12/22