Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore.
Google AI Healthcare, California, USA.
Prog Retin Eye Res. 2019 Sep;72:100759. doi: 10.1016/j.preteyeres.2019.04.003. Epub 2019 Apr 29.
The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally.
计算机图形处理单元的出现、数学模型的改进和大数据的可用性，使得人工智能 (AI) 能够利用机器学习 (ML) 和深度学习 (DL) 技术，在社交媒体、物联网、汽车行业和医疗保健等广泛领域实现强大的性能。特别是 DL 系统在图像、语音和运动识别以及自然语言处理方面提供了改进的能力。在医学领域，人工智能和 DL 系统在以图像为中心的专业领域取得了重大进展，如放射学、皮肤科、病理学和眼科学。新的研究，包括预先注册的前瞻性临床试验，表明 DL 系统在检测糖尿病视网膜病变 (DR)、青光眼、年龄相关性黄斑变性 (AMD)、早产儿视网膜病变、屈光不正以及识别心血管危险因素和疾病方面具有准确性和有效性，这些都是通过数字眼底照片实现的。人们也越来越关注使用 AI 和 DL 系统来识别视网膜疾病（如新生血管性 AMD 和糖尿病性黄斑水肿）的疾病特征、进展和治疗反应，这些疾病是使用光学相干断层扫描 (OCT) 识别的。此外，将 ML 应用于视野可能有助于检测青光眼的进展。将包括电子健康记录在内的临床数据纳入 AI 和 DL 算法的研究有限，也没有前瞻性研究表明 AI 和 DL 算法可以预测临床眼病的发展。本文描述了全球眼部疾病负担、未满足的需求以及公共卫生重要的常见疾病，人工智能和深度学习系统可能适用于这些疾病。讨论了构建 DL 系统以满足这些需求的技术和临床方面，以及临床采用的潜在挑战。人工智能、机器学习和深度学习很可能在临床眼科实践中发挥关键作用，对全球老龄化人口中主要视力损害原因的筛查、诊断和随访具有重要意义。
