The rise of non-invasive, rapid, and widely accessible quantitative high-resolution imaging methods, such as modern retinal photography and optical coherence tomography (OCT), has significantly impacted ophthalmology. These techniques offer remarkable accuracy and resolution in assessing ocular diseases and are increasingly recognized for their potential in identifying ocular biomarkers of systemic diseases. The application of artificial intelligence (AI) has been demonstrated to have promising results in identifying age, gender, systolic blood pressure, smoking status, and assessing cardiovascular disorders from the fundus and OCT images. Although our understanding of eye–body relationships has advanced from decades of conventional statistical modeling in large population-based studies incorporating ophthalmic assessments, the application of AI to this field is still in its early stages. In this review article, we concentrate on the areas where AI-based investigations could expand on existing conventional analyses to produce fresh findings using retinal biomarkers of systemic diseases. Five databases—Medline, Scopus, PubMed, Google Scholar, and Web of Science were searched using terms related to ocular imaging, systemic diseases, and artificial intelligence characteristics. Our review found that AI has been employed in a wide range of clinical tests and research applications, primarily for disease prediction, finding biomarkers and risk factor identification. We envisage artificial intelligence-based models to have significant clinical and research impacts in the future through screening for high-risk individuals, particularly in less developed areas, and identifying new retinal biomarkers, even though technical and socioeconomic challenges remain. Further research is needed to validate these models in real-world setting.

中文翻译：

---

使用人工智能算法从视网膜图像预测全身疾病

现代视网膜摄影和光学相干断层扫描（OCT）等非侵入性、快速且广泛使用的定量高分辨率成像方法的兴起对眼科产生了重大影响。这些技术在评估眼部疾病方面提供了卓越的准确性和分辨率，并且因其在识别全身性疾病的眼部生物标志物方面的潜力而得到越来越多的认可。人工智能 (AI) 的应用已被证明在识别年龄、性别、收缩压、吸烟状况以及从眼底和 OCT 图像评估心血管疾病方面具有良好的效果。尽管我们对眼睛与身体关系的理解比几十年来在大规模人群研究中结合眼科评估的传统统计模型有所进步，人工智能在该领域的应用仍处于早期阶段。在这篇综述文章中，我们重点关注基于人工智能的研究可以扩展现有传统分析的领域，以利用系统性疾病的视网膜生物标志物产生新的发现。使用与眼部成像、系统性疾病和人工智能特征相关的术语对五个数据库——Medline、Scopus、PubMed、Google Scholar 和 Web of Science 进行了搜索。我们的审查发现，人工智能已广泛应用于临床测试和研究应用，主要用于疾病预测、寻找生物标志物和风险因素识别。我们设想基于人工智能的模型通过筛查高风险个体，特别是在欠发达地区，在未来产生重大的临床和研究影响，并确定新的视网膜生物标志物，尽管技术和社会经济挑战仍然存在。需要进一步的研究来在现实环境中验证这些模型。