Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Clinical and Translational Sciences Program, Duke-NUS Medical School, Singapore.
Asia Pac J Ophthalmol (Phila). 2022;11(2):126-139. doi: 10.1097/APO.0000000000000515.
Despite the huge investment in health care, there is still a lack of precise and easily accessible screening systems. With proven associations to many systemic diseases, the eye could potentially provide a credible perspective as a novel screening tool. This systematic review aims to summarize the current applications of ocular image-based artificial intelligence on the detection of systemic diseases and suggest future trends for systemic disease screening.
A systematic search was conducted on September 1, 2021, using 3 databases-PubMed, Google Scholar, and Web of Science library. Date restrictions were not imposed and search terms covering ocular images, systemic diseases, and artificial intelligence aspects were used.
Thirty-three papers were included in this systematic review. A spectrum of target diseases was observed, and this included but was not limited to cardio-cerebrovascular diseases, central nervous system diseases, renal dysfunctions, and hepatological diseases. Additionally, one- third of the papers included risk factor predictions for the respective systemic diseases.
Ocular image - based artificial intelligence possesses potential diagnostic power to screen various systemic diseases and has also demonstrated the ability to detect Alzheimer and chronic kidney diseases at early stages. Further research is needed to validate these models for real-world implementation.
尽管在医疗保健方面投入巨大，但仍缺乏精确且易于获取的筛查系统。眼睛与许多系统性疾病有明确关联，因此有可能成为一种新颖的筛查工具。本系统评价旨在总结基于眼部图像的人工智能在系统性疾病检测中的当前应用，并为系统性疾病筛查提出未来趋势。
我们于 2021 年 9 月 1 日在 3 个数据库（PubMed、Google Scholar 和 Web of Science 图书馆）中进行了系统检索。未设置日期限制，使用了涵盖眼部图像、系统性疾病和人工智能方面的检索词。
本系统评价共纳入 33 篇论文。观察到一系列目标疾病，不仅限于心脑血管疾病、中枢神经系统疾病、肾功能障碍和肝脏疾病，此外，三分之一的论文还包括相应系统性疾病的风险因素预测。
基于眼部图像的人工智能具有筛查各种系统性疾病的潜在诊断能力，并且已经证明能够在早期检测阿尔茨海默病和慢性肾脏疾病。需要进一步的研究来验证这些模型在实际中的应用。
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