眼可受全身系统性疾病的累及，通过眼部表现可对心血管系统性疾病、神经系统疾病、自身免疫性疾病、感染性疾病及药物相关眼病等全身疾病进行评估、协助诊断和随访观察。重视全身疾病在眼部的表现，对于眼科及相关专业临床诊疗水平的提升具有重要意义。

Eyes can be affected by systemic diseases. Ocular manifestations can be used to evaluate, help to diagnose and observe systemic diseases including cardiovascular diseases, neurological disorders, autoimmune diseases, infectious diseases and drug-related eye diseases. Paying attention to the manifestations of systemic diseases in the eye is of great significance for the improvement of clinical diagnosis and treatment in ophthalmology and related specialties.

脂质代谢异常是糖尿病性视网膜病变可能的危险因素。糖尿病性视网膜病变被认为是致盲的主要原因。近年来研究认为总胆固醇、三酰甘油等血脂与糖尿病性视网膜病变及糖尿病黄斑水肿的进展有关，降脂药物的应用能够延缓糖尿病性视网膜病变进展。随着色谱分离和质谱分析等脂质组学分析方法的发展，除了常规的血清脂质标志物以外的各种脂质成分也被发现可能与糖尿病性视网膜病变进展有关。现总结脂质及其衍生物在糖尿病性视网膜病变发病机制中的作用，阐述糖尿病性视网膜病变脂质代谢治疗的潜在靶点和前景。

Abstract Abnormal lipid metabolism is a possible risk factor for diabetic retinopathy. Diabetic retinopathy is considered to be the main cause of blindness. In recent years, studies have shown that serum lipids, such as total cholesterol, triglycerides, are related to the progress of diabetic retinopathy and diabetic macular edema, and lipid-lowering drugs can delay the progress of diabetic retinopathy. With the development of lipidomics analysis methods such as chromatographic separation and mass spectrometry, lipid components other than conventional serum lipid markers have also been found to be related to the progression of diabetic retinopathy. The review summarizes the role of lipids and their derivatives in the pathogenesis of diabetic retinopathy, and highlights the potential targets and prospects of lipid metabolism treatment for diabetic retinopathy.

近年来人工智能(artificial intelligence，AI)技术在医学领域的应用发展迅猛，尤其在眼科领域，成果显著，极大地提高了相关影像数据的诊断效率，推动了该领域研究的进展。然而，大多数AI的应用都集中于成人眼病，在婴幼儿眼病方向的研究较少。究其原因，可能是婴幼儿眼部影像数据采集配合度低，部分影像设备应用受限，且相关领域专业眼科医生数量匮乏。然而，婴幼儿期是视觉发育最重要的阶段，也是出生缺陷早期筛防诊治的重灾区，对患儿的视觉发展具有长远且重要的影响，亟需AI相关产品提高婴幼儿眼病筛查效率，缓解医疗资源不足的现状。本文将对近年AI在婴幼儿眼病领域的研究应用现状、进展及存在的相关问题进行综述。

In recent years, the application of artificial intelligence (AI) in medicine, especially in ophthalmology, has developed rapidly with remarkable results. This has greatly improved the diagnostic efficiency of relevant imaging data and promoted further research in this field. However, most applications of AI are focused on adult eye diseases, and few studies have addressed infantile eye diseases. This may be because of the non-cooperative nature of infants, the limited availability of imaging equipment in infants, and the lack of pediatric ophthalmologists. Infancy is the most important stage of vision development. Disturbance during this period have a profound and lasting influence on vision development. Hence, early screening, diagnosis, and treatment of birth defects is important. AI-related products, which improves the efficacy of infant eye disease screening, are urgently needed. This paper reviews the current status, progress, and existing problems of recent research related to application of AI in infantile eye diseases.