Abstract：
We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.
摘要：
我们对视网膜眼底摄影图像的人工智能（AI）研究进行了系统回顾。我们强调了各种人工智能算法的使用，包括深度学习(DL)模型，用于眼科和非眼科应用(即，系统性)障碍。我们发现使用人工智能算法来解释视网膜图像，与临床数据和医生专家相比，代表了一种创新的解决方案，在识别许多眼科(例如，糖尿病视网膜病变（DR），年龄相关性黄斑变性（AMD），视神经疾病)，和非眼科疾病(例如，痴呆症,心血管疾病）。这项研究有大量的临床和影像学数据，导致AI和DL的潜在整合，以进行自动化分析。人工智能有潜力通过提高准确性来改变医疗保健，速度,和工作流，降低成本,越来越多的访问，减少错误,并转变医护人员的教育和培训。
