随着人工智能(artificial intelligence，AI)技术的快速发展，基于深度学习(deep learning，DL)和机器学习的AI技术在医学领域上的应用受到了广泛的关注。AI在眼科的应用也逐渐向更全面更深入的层次发展，通过角膜断层扫描、光学相干断层扫描、裂隙灯图像等技术，AI在对角膜病变、结膜病变、白内障、青光眼等眼部疾病的诊断和治疗方面都表现出了良好的性能。然而AI在眼科的应用方面也存在一些诸如结果可解释性的欠缺、数据集标准化的缺乏、数据集质量的不齐、模型适用性的不足和伦理问题等挑战。在5G和远程医疗飞速发展的时代，眼科AI同时也有许多新的机遇。本文综述了AI在前段眼科疾病中的应用、临床实施的潜在挑战和前景，为AI在眼科领域的进一步发展提供参考信息。

With the rapid development of artificial intelligence (AI) technology, the application of AI technology based on deep learning (DL) and machine learning (ML) in the medical field has received widespread attention. The application of AI in ophthalmology is gradually being shifted to a more comprehensive and in-depth level. Trained on corneal tomography, optical coherence tomography (OCT), slit-lamp images, and other techniques. AI can achieve robust performance in the diagnosis and treatment of corneal lesions, conjunctival lesions, cataract, glaucoma and other ophthalmic diseases. However, there are also some challenges in the application of AI in ophthalmology, including the lack of interpretability of results, lack of standardization of data sets, uneven quality of data sets, insufficient applicability of models and ethical issues. In the era of 5G and telemedicine, there are also many new opportunities for ophthalmic AI. In this review, we provided a summary of the state-of-the-art AI application in anterior segment ophthalmic diseases, potential challenges in clinical implementation and its development prospects, and provides reference information for the further development of artificial intelligence in the field of ophthalmology.

视网膜是中枢神经系统的一部分。在胚胎起源上，视网膜和大脑均由神经管发育而来。因此，许多发生在大脑的神经退行性疾病往往会同时累及视网膜。而神经退行性疾病过程中相关的特征性病理改变，如病理性蛋白聚集和神经血管单元破坏也常能在视网膜组织中被检测到。在一些神经退行性疾病中，眼部的病理改变甚至在临床症状出现之前就已发生；其次视网膜易于观察且局部治疗操作便捷，因此近年来视网膜在中枢神经退行性疾病发病机制研究、早期诊断和新型治疗方式探究等方面备受关注。该文对常见神经退行性疾病的眼部病理改变进行综述，旨在为大脑和视网膜神经退行疾病的发病机制、诊断以及治疗研究提供新的见解。

The retina is a part of the central nervous system. Developmentally, both retina and brain are derived from the neural tube. Therefore, many neurodegenerative diseases that occur in the brain tend to involve both the retina. In the process of neurodegenerative diseases, related characteristic pathological changes, such as pathological protein aggregation, neurovascular unit impairment can often be detected in retinal tissue. In some neurodegenerative diseases, pathological changes in the eye occur even before clinical symptoms appear. In addition, the retina are easy to observe and local treatments are convenient. In recent years, the manifestations of the retina have attracted much attention in the study of pathogenesis, early diagnosis, and new treatments of systemic central neurodegenerative diseases. In this way, this article reviews the ocular pathological changes of common neurodegenerative diseases, aiming to provide new insights into the pathogenesis, diagnosis, and treatment of brain and retinal neurodegenerative diseases.

作为一种新型无创且操作简单的主观检查手段，临界闪烁融合频率(critical flicker fusionfrequency,CFF)可动态反映人眼视功能变化情况。作为早期识别脱髓鞘病变和评估视功能恢复情况的敏感指标，上个世纪已被国外学者用于视网膜和视神经疾病研究中，包括氯喹中毒性视网膜病变、糖尿病视网膜病变、中心性浆液性视网膜病变、年龄相关的黄斑病变、乙胺丁醇中毒性视神经病变、视神经炎和非动脉炎性前部缺血性视神经病变。在视网膜和视神经疾病中，CFF均有不同程度下降，依据CFF改善程度以及主要损害的色光可能有助于视网膜和视神经疾病的鉴别，且CFF与其他视功能，视力、视野、视觉诱发电位的潜时具有较好的相关性。目前国内相关研究尚处于起步阶段，本文就CFF在视网膜和视神经疾病的应用情况做一总结。

As a new non-invasive and simple subjective examination method, critical flicker fusion frequency (CFF) can dynamically reflect the changes of visual function of human eyes. As a sensitive indicator for early identification of demyelinating diseases and assessment of visual function recovery, it has been used by foreign scholars in the last century in the field of retinal and optic nerve diseases, including chloroquine toxic retinopathy, diabetic retinopathy, central serous retinopathy, age-related macular degeneration, ethambutol-induced optic neuropathy, optic neuritis and non-arteritic anterior ischemic optic neuropathy. Though there was a different decrease of CFF in retina and optic nerve diseases, it may be helpful for the differentiation of retinal and optic nerve diseases according to the degree of CFF improvement and the main damaged color light. Moreover, CFF has a good correlation with other visual functions, visual acuity, visual field, and peak time of visual evoked potential. At present, and relevant domestic studies is still in its infancy. This article summarizes the application of CFF in retinal and optic nerve diseases.

人工智能是对人类智能的模拟和拓展。基于深度学习的人工智能可以很好地利用图像的内在特征，如轮廓、框架等，来分析图像。研究人员通常利用图像来诊断眼底病，因此将人工智能应用于眼底检查是有意义的。在眼科领域，人工智能通过分析光学相干断层扫描图像、眼底照片和超宽视野图像，已经在检测多种眼底疾病上取得了类似医生的性能。它也已经被广泛应用于疾病进展预测。然而，人工智能在眼科的应用也存在一些潜在的挑战，黑盒问题是其中之一。研究人员致力于开发更多的可解释的深度学习系统，并确认其临床可行性。人工智能在最流行的眼底病中的最新应用、可能遇到的挑战以及未来的道路将一一阐述。

Artificial intelligence (AI) is about simulating and expanding human intelligence. AI based on deep learning (DL) can analyze images well by using their inherent features, such as outlines, frames and so on. As researchers generally diagnoses ocular fundus diseases by images, it makes sense to apply AI to fundus examination. In ophthalmology, AI has achieved doctor-like performance in detecting multiple ocular fundus diseases through optical coherence tomography (OCT) images, fundus photographs, and ultra-wide-field (UWF) images. It has also been widely used in disease progression prediction. Nonetheless, there are also some potential challenges with AI application in ophthalmology, one of which is the black-box problem. Researchers are devoted to developing more interpretable deep learning systems (DLS) and confirming their clinical feasibility. This review describes a summary of the state-of-the-art AI application in the most popular ocular fundus diseases, potential challenges and the path forward.

近年来随着人口老龄化的发展、人群用眼方式的改变，现有的眼科医疗资源正越来越难以满足日渐增长的医疗需求，亟需新型的诊疗模式予以补足。眼科人工智能作为眼科领域的新兴元素，在眼病的筛查诊断中发展迅速，主要表现为“眼部图像数据+人工智能”的模式。近年来，随着该模式在白内障、青光眼、糖尿病性视网膜病变(diabetic retinopathy，DR)等常见病中研究的深入，相关技术日渐成熟，表现出了较大的应用优势与应用前景，部分技术甚至成功转化并被逐渐应用于临床。眼科诊疗向智慧医学模式的过渡，有望缓解日益增长的医疗需求与紧缺的医疗资源之间的矛盾，从而提高整体的医疗服务水平。

The development of population aging and changes in the way people use their eyes over the recent years have increasingly challenged the existing ophthalmic medical resources to meet the growing medical needs, thus urgently calling for a novel diagnostic and treatment mode. Despite its status as an emerging sector in ophthalmology, ophthalmic artificial intelligence has developed rapidly in the screening and diagnosis of eye diseases, as can be seen in practices adopting the “eye imaging data + AI” mode. In recent years, with the intensified research on this mode with respect to common diseases such as cataract, glaucoma and diabetic retinopathy, relevant technologies have grown increasingly mature, presenting undeniable application superiority and prospects. Some of the relevant technical achievements have also been successfully transformed for practical usage, and are gradually being applied to clinical practices. Ophthalmic diagnosis and treatment are transitioning toward the era of intelligent medical services, which are expected to reduce the contradictions between the growing medical needs and the shortage of medical resources, as well as ultimately improve the overall experience of medical services.

目的：分析单中心神经眼科疾病谱及流行病学特点，为指导神经眼科疾病诊断和治疗提供基础。方法：纳入2010年1月1日—2021年12月31日中国人民解放军总医院神经眼科病区收治的神经眼科疾病患者，从电子病例系统检索和记录所有纳入病例的年龄、性别、地区分布及病种亚型分析。结果：共计7245例神经眼科患者纳入统计，其中男性3331例(46.0%)、女性3914例(54.0%)，男女比例1∶1.2；年龄(38.2±17.5)岁。83.25%(6031/7245)为传入神经系统疾病，9.92%(719/7245)为传出神经系统疾病和眼眶疾病，6.83%(495/7245)未归类。病种分析显示，占比最高的是脱髓鞘性视神经炎(demyelinating optic neuritis,DON)，为40.17%(2910/7245)；占比第二的是非动脉炎性前部缺血性视神经病变(nonarteritic anterior ischemic optic neuropathy,NAION)，为11.37%(824/7245)；占比第三的是外伤性视神经病变5.15%(373/7245)，其中7.85%(569/7245)表现为不明原因视神经萎缩。从年龄分布来看，DON和外伤性视神经病变患者中18~40岁者占比最高(分别为48.63%和44.24%)，NAION患者中41~60岁者占比最高(66.14%)，小于18岁的未成年患者在遗传性视神经病变中占比最高，比例为48.58%。在2226例DON患者中，视神经脊髓炎(neuromyelitis optica,NMO)/视神经脊髓炎谱系疾病(neuromyelitis optica spectrum disorder,NMOSD)比例最高，为60.02%；髓鞘少突胶质细胞糖蛋白抗体(myelinoligodendrocyte glycoprotein antibody,MOG-IgG)阳性视神经炎比例为11.68%；多发性硬化(multiple sclerosis,MS)和MS相关性视神经炎和慢性复发性炎性视神经病变(chronic recurrent inflammatory optic neuropathy,CRION)占比较低，分别是1.8%和2.25%。DON整体患者中，男女比例为1∶3.08；在NMO/NMOSD患者中男女比例为1∶8；MOG阳性视神经炎患者中，男女比例为4∶5；在非典型视神经炎患者中，男性比例高于女性，为1.28∶1；DON患者中，81.79%患者为中青年，MOG阳性视神经炎未成年患者可达41.15%。结论：DON和NAION是神经眼科传入系统疾病最常见两大病种。

Objective: To analyze the spectrum and epidemiological characteristics of neuro-ophthalmic diseases from single center, and to provide basis for guiding the diagnosis and treatment of neuro-ophthalmic diseases. Methods: Patients with neuro-ophthalmic diseases admitted to the neuro-ophthalmology ward of Chinese PLA General Hospital from January 1, 2010 to December 31, 2021 were enrolled. The age, gender, regional distribution and disease subtypes of all included patients were retrieved and recorded from the electronic case system. Results: A total of 7245 patients with neuro-ophthalmic diseases were enrolled, including 3331 males(46.0%)and 3914 females(54.0%), with a male to female ratio of 1:1.2. The average age was 38.2±17.5 years. 83.25%(6031/7245)were afferent nervous system diseases, 9.92% (719/7245)were efferent nervous system diseases and orbital diseases, and 6.83%(495/7245)were not classified. The ratio of demyelinating optic neuritis(DON)was the highest(40.17%,2910/7245), followed by nonarteritic anterior ischemic optic neuropathy(NAION)(11.37%,824/7245)and traumatic optic neuropathy(TON) (5.15%,373/7245). The ratio of optic nerve atrophy with unknown causes was 7.85%(569/7245). Characteristics of age distribution, the DON and TON were more common in 18-40 age group(the proportion were 48.63% and 44.24%,respectively), the NAION was common in 41-60 age group(66.14%), and the hereditary optic neuropathy was common in younger 18 age group (48.58%). In 2226 DON patients, the proportion of neuromyelitis optica(NMO)/neuromyelitis optica spectrum disorder(NMOSD)-optic neuritis(ON)was the highest(60.02%)and myelinoligodendrocyte glycoprotein antibody(MOG-IgG)ON was 11.68%, while multiple sclerosis(MS)-ON and chronic recurrent inflammatory optic neuropathy(CRION)were relatively low(1.8% and 2.25%,respectively). In DON patients, the male to female ratio was 1:3.08. In NMO/NMOSD-ON patients, the ratio of male to female was 1:8, and that of MOG-ON was 4:5. In atypical ON, the ratio of male to female was higher than that of female(1.28:1). In DON patients, 81.79% of patients were young and middle-aged, and the proportion of children with MOG-ON(less than 18 years old)was 41.15%.Conclusions: DON and NAION are the two most common diseases of neuro-ophthalmic afferent system.

视网膜神经纤维层是视网膜的最内层，主要由来自视网膜神经节细胞的无髓鞘轴突组成，此外还有神经胶质细胞与视网膜血管，其厚度与年龄、眼球增长、眼底结构改变等因素相关。光学相干断层扫描可以清晰展示角膜、视网膜、脉络膜、视神经等高分辨率断层图像，可以在活体上显示生物学组织的细微结构，在临床与科研中已获得广泛应用。在青光眼视神经病变中，光学相干断层扫描可以发现视野异常前的视网膜神经纤维层损害，已成为青光眼早期诊断与视神经损伤程度检测的重要手段。除视神经病外，越来越多的研究表明许多视网膜血管疾病、神经元变性疾病等视网膜疾病也有视网膜神经纤维层的损伤。探讨视网膜疾病与神经纤维层的关系，将有利于进一步推进对视网膜疾病发病机制及病理改变的认识。本文就视网膜神经纤维层的定量评估与多种视网膜疾病的关系展开综述，为其在视网膜疾病中的应用提供参考。

The retinal nerve fiber layer, the innermost layer of the retina, consists mainly of unmyelinated axons from retinal ganglion cells, as well as glial cells and retinal blood vessels , the thickness of which is related to factors such as age, ocular growth and fundus structure changes. Optical coherence tomography (OCT) can clearly display the cornea, retina, choroid, optic nerve and other high-resolution tomography images. It can show the fine structure of biological tissues in vivo, which has been widely used in clinical and scientific research. In glaucomatous optic neuropathy, OCT can detect the damage of retinal nerve fiber layer before abnormal visual field, which has become an important means of early diagnosis of glaucoma and detection of the degree of optic ner ve damage. In addition to optic neuropathy, more studies have shown that many retinal diseases such as retinal vascular diseases and neurodegenerative diseases also have retinal nerve fiber layer injury. Exploring the relationship between retinal diseases and nerve fiber layer will be beneficial to further promote the understanding of the pathogenesis and pathological changes of retinal diseases. This paper reviews the relationship between the quantitative evaluation of retinal nerve fiber layer and various retinal diseases, and provides reference for its application in retinal diseases.

眼表菌群是定植于眼表的各种微生物群落，以细菌为主。在正常情况下，眼表菌群与人体眼表组织的细胞和平共生，维持眼表的稳态，共同保证眼表的健康。但在环境改变或免疫力低下的情况下，眼表菌群会发生变化，与眼部疾病的产生与发展关系密切，对人类的健康造成巨大的危害。随着组学研究的不断发展，我们对眼表菌群有了新的认识，为眼表疾病的发病机制、治疗开辟了新的思路，同时也提出了新的挑战。本文对国内外眼表菌群与疾病关系进行综述，为眼表疾病的发生、发展以及治疗提供参考。

The microbiome of the ocular surface consists of various microbial communities that colonize on the eye surface, mainly bacteria. The stabilization of the microbiome and the other ocular surface components plays an important role in maintaining the homeostasis of the ocular surface. However, unpredictable changes of ocular surface microbiome are strongly associated with ocular surface diseases in the situation of environmental changes or destruction of immune system. With the innovation of inspection technology, the current gene sequencing technology is applied to detect the ocular surface microbiome and confirm that the eye microbiome is closely related to ocular surface diseases. This paper investigates the corelation of ocular surface microbiomes and diseases. Moreover, we provide areference for the occurrence and development of ocular surface diseases and their treatment.

阿尔茨海默病(Alzheimer’s disease，AD)是发生于老年期或老年前期的中枢神经系统退行性病变，以进行性认知功能障碍为特征。随着社会老龄化加剧，AD已成为全球公共卫生问题，亟需研发更敏感、便捷和经济的筛查技术进行早期防控。眼球运动与认知功能密切相关，且眼球运动检查有非侵入性、成本低、检查时间短等优点。研究眼球运动异常和认知功能障碍之间的相关性，有助于研发更简便易操作的认知功能障碍筛查工具。随着人工智能技术的发展，机器学习算法强大的特征提取和计算能力对处理眼球运动检查结果有显著优势。本文对既往AD患者与眼球运动异常之间的相关性研究进行综述，并对机器学习算法模型辅助下，基于眼球运动异常模式进行认知功能障碍早期筛查技术开发的研究前景予以展望。

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system that occurs in old age or early old age. It is characterized by progressive cognitive dysfunction. With the world population aging, AD has become a global public health problem. The development of a more sensitive, convenient, and economic screening technology for AD is urgently needed. The eye movement function is closely related to cognitive function. Moreover, eye movement examination has advantages including non-invasiveness, low cost, and short examination time. Researches on the correlation between abnormal eye movement and cognitive dysfunction can help to develop a simple and easy-to-use screening tool for cognitive dysfunction. With the development of artificial intelligence technology, the dominant feature extraction and computing capabilities of machine learning algorithms have a significant advantage in processing eye movement inspection results. This article reviews the correlation between AD and eye movement abnormalities aiming to provide the research prospects of early screening technology development for cognitive dysfunction based on abnormal eye movement with the application of machine learning models.

近年来随着医疗领域数字化、信息化建设的加速推进，人工智能的应用越来越广泛，在眼科医学方面尤为突出。婴幼儿处于视觉系统发育的关键时期，此时发生的眼病往往会造成不可逆的视功能损伤，带来沉重的家庭和社会负担。然而，由于婴幼儿群体的特殊性以及小儿眼科医生的短缺，开展大规模小儿眼病筛查工作十分困难。最新研究表明：人工智能在先天性白内障、先天性青光眼、斜视、早产儿视网膜病变以及视功能评估等领域已经得到相关应用，在多种婴幼儿眼病的早期筛查、诊断分期、治疗建议等方面都有令人瞩目的表现，有效解决了许多临床难点与痛点。但目前婴幼儿眼科人工智能仍然不如成年人眼科发展充分，亟须进一步的探索和研发。

In recent years, with the acceleration of digitalization and informatization in medical field, artificial intelligence (AI) is more and more widely applied, especially in ophthalmology. Infants are in the critical period of visual development, during which eye diseases can lead to irreversible visual impairment and bring heavy burden to family and society. Due to the particularity of infants and the shortage of pediatric ophthalmologists, it is challenging to carry out large-scale screening for eye diseases of infants. According to the latest studies, AI has been studied and applied in the fields of congenital cataract, congenital glaucoma, strabismus, amblyopia, retinopathy of prematurity, and evaluation of visual function, and it has achieved remarkable performance in the early screening, diagnosis stage and treatment suggestions, solving many clinical difficulties and pain points effectively. However, AI for infantile ophthalmology is not as developed as for adult ophthalmology, so it needs further exploration and development.