Application of artificial intelligence in cataract management: current and future directions

Eye and Vision

Darren Shu Jeng Ting¹  Laura Gutierrez²  Gilbert Yong San Lim²  Michelle Yip³  Jodhbir Singth Mehta³  Daniel Shu Wei Ting³  Jane Sujuan Lim³  Li Lian Foo³  Mohamad Rosman³  Wei Yan Yan Ng³  Allan Fong⁴  Melissa Hsing Yi Wong⁴  Haotian Lin⁵ 

[1] Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, UK;Singapore Eye Research Institute, Singapore, Singapore;Singapore Eye Research Institute, Singapore, Singapore;Singapore National Eye Center, 11 Third Hospital Avenue, 168751, Singapore, Singapore;Singapore National Eye Center, 11 Third Hospital Avenue, 168751, Singapore, Singapore;Zhongshan Ophthalmic Center, Sun Yet Sen University, Guangzhou, China;

关键词: Artificial intelligence;    Telemedicine;    Cataract;    Cataract screening;    Cataract surgery;    IOL calculations;    Biometry;    Machine learning;   

DOI  :  10.1186/s40662-021-00273-z

来源: Springer

【 摘 要 】

The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract  is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. This utilizes a deep-learning, convolutional neural network (CNN) to detect and classify referable cataracts appropriately. Second, some of the latest intraocular lens formulas have used AI to enhance prediction accuracy, achieving superior postoperative refractive results compared to traditional formulas. Third, AI can be used to augment cataract surgical skill training by identifying different phases of cataract surgery on video and to optimize operating theater workflows by accurately predicting the duration of surgical procedures. Fourth, some AI CNN models are able to effectively predict the progression of posterior capsule opacification and eventual need for YAG laser capsulotomy. These advances in AI could transform cataract management and enable delivery of efficient ophthalmic services. The key challenges include ethical management of data, ensuring data security and privacy, demonstrating clinically acceptable performance, improving the generalizability of AI models across heterogeneous populations, and improving the trust of end-users.

【 授权许可】

CC BY