| Literature DB >> 32532762 |
Darren Shu Jeng Ting1,2,3, Valencia Hx Foo4, Lily Wei Yun Yang5, Josh Tjunrong Sia6, Marcus Ang3,7, Haotian Lin8, James Chodosh9, Jodhbir S Mehta3,7, Daniel Shu Wei Ting10,11.
Abstract
With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for 'intelligent' healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions. In this review, we provide an overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour, and highlight important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions. © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.Entities:
Keywords: conjunctiva; cornea; glaucoma; lens and zonules; telemedicine
Year: 2020 PMID: 32532762 DOI: 10.1136/bjophthalmol-2019-315651
Source DB: PubMed Journal: Br J Ophthalmol ISSN: 0007-1161 Impact factor: 4.638