Gutierrez Laura, Lim Jane Sujuan, Foo Li Lian, Ng Wei Yan, Yip Michelle, Lim Gilbert Yong San, Wong Melissa Hsing Yi, Fong Allan, Rosman Mohamad, Mehta Jodhbir Singth, Lin Haotian, Ting Darren Shu Jeng, Ting Daniel Shu Wei
Singapore Eye Research Institute, Singapore, Singapore.
Singapore National Eye Center, 11 Third Hospital Avenue, Singapore, 168751, Singapore.
Eye Vis (Lond). 2022 Jan 7;9(1):3. doi: 10.1186/s40662-021-00273-z.
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.
人工智能（AI）的兴起在医学的许多领域都带来了突破。在眼科领域，人工智能在糖尿病性视网膜病变、年龄相关性黄斑变性、青光眼和早产儿视网膜病变的筛查与检测方面取得了显著成果。白内障治疗是另一个可以从人工智能更多应用中受益的领域。白内障是导致可逆性视力损害的主要原因，全球临床负担不断增加。改善诊断、监测和手术管理对于应对这一挑战至关重要。此外，大型发展中国家的患者往往难以获得三级医疗服务，而持续的新冠疫情使这一问题更加严重。另一方面，人工智能可以通过提高自动化程度、疗效并克服地理障碍来帮助变革白内障治疗。首先，人工智能可以用作远程诊断平台，利用裂隙灯和眼底照片对白内障患者进行筛查和诊断。这利用深度学习卷积神经网络（CNN）来适当地检测和分类可转诊的白内障。其次，一些最新的人工晶状体公式使用人工智能来提高预测准确性，与传统公式相比，术后屈光效果更佳。第三，人工智能可用于通过识别视频中白内障手术的不同阶段来加强白内障手术技能培训，并通过准确预测手术时间来优化手术室工作流程。第四，一些人工智能CNN模型能够有效预测后囊膜混浊的进展以及最终进行YAG激光后囊切开术的必要性。人工智能的这些进展可以变革白内障治疗，并实现高效眼科服务的提供。关键挑战包括数据的伦理管理、确保数据安全和隐私、证明临床可接受的性能、提高人工智能模型在异质人群中的通用性，以及提高终端用户的信任度。
