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.

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.

目的：依托最新的第5代移动通信技术(5th generation wireless systems，5G)，构建基于眼底图片的5G医疗眼科远程诊断平台，促进医疗资源上下贯通，提升基层服务能力及医疗服务体系整体效能。方法：基于5G时代医院的信息化发展战略，在海南省卫生健康委员会的资助与指导下，中山大学中山眼科中心海南眼科医院与中国联通通信集团海南有限公司等进行跨行业、多学科的技术力量研究开发，构建5G条件下的平台建设模块和技术路线，确定远程眼科诊断流程，并在海南省内多地区应用。结果：远程诊断平台运行良好。2020年12月至2021年11月，本研究共在海南省17个地区的186个卫生院中开展，共收集1561例患者眼底病图片数据，筛查阳性例数为185例，检出眼底病总阳性率为11.9%。其中有42例需要转诊治疗，转诊率为23%；143例不需要转诊治疗，非转诊率为77%。在1561例眼底图像中，采集异常的眼底图像有490例。排除490例异常眼底图像后，辅助诊断系统与人工诊断结果有1 002张眼底图像诊断相同，69张眼底图像诊断不同，其辅助诊断系统准确率为93.3%。结论：5G移动通信与远程医学影像结合，运用互联网科技催生新型医疗生产力，提高卫生经济的质量和效率，是医疗领域探索5G应用场景的一项应用典范。

Objective: Relying on the latest 5th generation wireless systems (5G), a remote primary ophthalmology care diagnosis platform based on fundus images was constructed in order to promote the connectivity of medical resources and improve the primary health service capabilities and the overall effectiveness of the medical service system. Methods: Based on the 5G informatization development strategy of hospitals, and under the funding and guidance of the Hainan Provincial Health Commission, the Hainan Eye Hospital of Zhongshan Ophthalmic Center and China Unicom Communications Group Hainan Co., Ltd. conducted a cross-industry, multi-disciplinary technical research. To build platform construction modules and technical routes under 5G networks, present the remote ophthalmological diagnosis process, and apply it in many regions in Hainan Province. Results: The performance of the remote diagnosis platform is well. From December 2020 to November 2021, this study was carried out in 186 health centers in 17 regions of Hainan Province. A total of 1 561 patients with fundus disease image data were collected. The number of positive screening fundus disease cases was 185. The total positive rate was 11.9%. Among them, 42 cases required referral for treatment, with a referral rate of 23%, and 143 cases did not require referral for treatment, with a non-referral rate of 77%. Among 1 561 cases of fundus images, 490 fundus images were excluded due to abnormal quality. Compared the results of the diagnosis platform system with manual diagnosis, 1 002 fundus images were identical, and 69 fundus images were different in diagnosis. The accuracy of the auxiliary diagnosis system was 93.3%. Conclusions: The collaboration of 5G mobile communication and telemedicine imaging, combined with internet technology to promote new medical productivity, improve quality and efficiency of the health economy. This study is an application model for exploring 5G application scenarios in the medical field.

全身疾病通过一定途径累及眼球，产生眼部病变，这些眼部病变的严重程度与全身疾病的进展密切相关。人工智能(artificial intelligence，AI)通过识别眼部病变，可以实现对全身疾病的评估，从而实现全身疾病早期诊断。检测巩膜黄染程度可评估黄疸；检测眼球后动脉血流动力学可评估肝硬化；检测视盘水肿，黄斑变性可评估慢性肾病(chronic kidney disease，CKD)进展；检测眼底血管损伤可评估糖尿病、高血压、动脉粥样硬化。临床医生可以通过眼部影像评估全身疾病的风险，其准确度依赖于临床医生的经验水平，而AI识别眼部病变评估全身疾病的准确度可与临床医生相媲美，在联合多种检测指标后，AI模型的特异性与敏感度均可得到显著提升，因此，充分利用AI可实现全身疾病的早诊早治。

Systemic diseases affect eyeballs through certain ways, resulting in eye diseases; The severity of eye diseases is closely related to the progress of systemic diseases. By identifying eye diseases, artificial intelligence (AI) can assess systemic diseases, so as to make early diagnosis of systemic diseases. For example, detection of the degree of icteric sclera can be used to assess jaundice. Detection of the hemodynamics of posterior eyeball can be used to evaluate cirrhosis. Detection of optic disc edema and macular degeneration can be used to evaluate the progress of chronic kidney disease (CKD). Detection of ocular fundus vascular injury can be used to assess diabetes, hypertension and atherosclerosis. Clinicians can estimate the risk of systemic diseases through eye images, and its accuracy depends on the experience level of clinicians, while the accuracy of AI in identifying eye diseases and evaluating systemic diseases can be comparable to clinicians. After combining various detection indexes, the specificity and sensitivity of AI model can be significantly improved, so early diagnosis and early treatment of systemic diseases can be realized by making full use of AI.

白内障是世界范围内致盲的主要原因之一，占中低收入国家致盲病例的50%。随着人口老龄化程度的加深，到2050年中国白内障致盲病例预计达到2 000万。卫生支出占比低、医疗设备及眼科医生紧缺、筛查费用昂贵仍是中低收入国家无法开展大规模白内障筛查的主要原因。人工智能(artificial intelligence，AI)协助白内障诊断具有便捷、低成本、可远程进行等优点，有望减少甚至避免白内障致盲的发生。文章将对AI通过结合裂隙灯眼前节图像、眼底照片及扫频源光学相干层析图像进行白内障自动诊断等研究进行简要综述。

Cataract is a primary cause of blindness globally, particularly accounting for 50% of blindness cases in low- and middle- income countries. As the population ages, it is predicated that cataract blindness cases in China will rise to 20 million by 2050. However, low health expenditures, scarcity of medical equipment and ophthalmologists, and high screening costs continue to hinder mass cataract screening in these countries. Artificial intelligence(AI)-assisted cataract diagnosis offers significant advantages, including convenience, cost-effectiveness, and remote accessibility, potentially reducing or even eliminating cataract blindness. This review aims to concisely summarize the research on automatic cataract diagnosis utilizing AI, incorporating slit lamp images of anterior eye segment, fundus photographs, and swept source optical coherence tomography images.

目的：探索和实践人工智能辅助诊断的闵行区视觉健康管理模式。方法：介绍闵行区人工智能辅助诊断的视觉健康管理模式；分析对比传统视觉健康管理模式和人工智能辅助诊断视觉健康管理模式下，社区视觉健康筛查情况，眼病发现、需转诊、复诊情况等；工作人员配置、眼科门诊接诊情况、居民眼病知识率和视觉健康服务满意情况等。结果：传统视觉健康管理模式和人工智能辅助诊断模式主要眼病（糖尿病视网膜病变、青光眼、年龄相关性黄斑变性、高度近视）发现率比较差异均有统计学意义（c²=954.03，P ＜0.01），需转诊率差异有统计学意义（c²=431.07，P ＜0.01）。人工智能辅助诊断管理模式与传统视觉健康管理模式的居民在青光眼的知晓率比较差异有统计学意义（c²=4.24，P＜0.05）。传统视觉健康管理模式和人工智能辅助诊断模式居民对视觉健康服务中的服务质量和服务时间的满意度比较差异有统计学意义（Z=-2.75，Z=-2.18，P ＜0.05）。结论：人工智能辅助诊断视觉健康管理模式，糖尿病视网膜病变、青光眼等主要眼病发现率高于传统模式，需转诊率降低，居民对青光眼的知晓率提升，在服务质量和服务时间上的居民满意度较高。基于人工智能辅助诊断的视觉健康筛查与管理模式值得本区其他社区的推广和应用。

Objective To explore and practice the visual health management mode of Minhang District . Methods Introduce the visual health management mode of AI-assisted diagnosis in Minhang District; analyze and compare the traditional visual health management mode and AI-assisted visual health management mode, community visual health screening projects and completion conditions, screening files, eye disease discovery , referral, actual referral and return ; analyze and compare the visual health management mode, staff allocation, ophthalmic outpatient reception, and the satisfaction of visual health service. Results The difference in the discovery rate of major eye diseases (Diabetic Retinopathy,Glaucoma,Age-related Macular Degeneration,High Myopia) between the traditional model and artificial intelligence-assisted diagnosis mode in both communities (c²=954.03, P<0.01), the referral rate (c²=431.07, P<0.01). The awareness of AI-assisted diagnosis management improved in glaucoma in the two modes was statistically significant (c²=4.24, P<0.05). Traditional model and artificial intelligence assisted diagnosis model of visual health service quality and service time is statistically significant (Z=-2.75, Z=-2.18, P<0.05). Conclusion The visual health screening and management mode based on AI-assisted diagnosis is worthy of the promotion and application in other communities in the region.

目的：探讨爱先蓝(Alcian Blue，AB)和细胞角蛋白(cytokeratin，CK)双重染色技术在泪腺腺样囊性癌组织中的应用, 提高泪腺腺样囊性癌染色效率。方法：选取中山大学中山眼科中心临床病理科2015年1月至2017年1月期间诊断为泪腺腺样囊性癌病例标本23例，在同一张切片上先进行AB染色，再进行CK染色，观察染色效果。结果：23例泪腺腺样囊性癌标本组织中黏液物质全部呈蓝色；癌细胞胞质CK阳性，呈棕黄色。结论：AB和CK双重染色方法稳定，颜色对比鲜明，能够良好显示癌细胞及黏液的关系，并且比分开的两次单种染色省时、经济。

Objective: To investigate the application of Alcian Blue (AB) and cytokeratin (CK) double staining technique in adenoid cystic carcinoma to improve the staining efficiency. Methods: Twenty-three specimens of adenoid cystic carcinoma from January 2015 to January 2017 in the Clinical Pathology Department of Zhongshan Ophthalmic Center, Sun Yat-sen University were stained with AB first and CK then on the same slide. Results: Mucinous substance showed blue. And the cytoplasm of the cancer cells presented with brown yellow in all the specimens.Conclusion: The method of double staining with AB and CK is stable and displays bright color contrast. It can effectively reveal the relationship between the mucus and cancer cells. It proves to be more time-saving and economical than the individual staining of AB and CK respectively.

眼附属器淋巴组织增生性疾病作为一类疾病的总称，包括了良性淋巴组织增生、非典型性淋巴组织增生、IgG4相关眼病以及多种恶性淋巴瘤在内的数十种疾病类型。临床诊断此类疾病应将患者眼部体征、影像学检查与病理学检查紧密结合。随着免疫表型及分子病理等检测技术的进步，此类疾病之间的鉴别诊断正逐渐清晰。本文就眼附属器淋巴组织增生性疾病进行系统性描述，并重点探讨该类疾病的病理鉴别诊断。

Ocular adnexal lymphoproliferative disease, as a general term, contains reactive lymphoid hyperplasia, atypical lymphoid hyperplasia, IgG4 related ocular disease and malignant lymphoma. The clinical diagnosis of this kind of disease should integrate patient’s symptoms, imaging features and pathology characteristics. Development of immunophenotyping, molecular pathology and other detection technology will help with the differential diagnosis of ocular adnexal lymphoproliferative disease. This article is going to discuss the etiology, epidemiology,diagnosis and treatment of ocular adnexal lymphoproliferative disease, with a focus on the clinicopathological differential diagnosis of such disease.

目的：比较活体共聚焦显微镜和病理检查在角膜后部真菌感染的诊断阳性率，探讨两种检查方法在角膜后部真菌感染诊断中的价值。方法：回顾性病例对照研究。收集2009年11月至2020年12月在青岛眼科医院就诊并进行穿透性角膜移植手术治疗角膜后部真菌感染患者，术前均进行角膜刮片KOH涂片检查和活体共聚焦显微镜检查，术后病变角膜进行病理组织切片、过碘酸-Schiff法(PAS)染色和六亚甲基四胺银法(GMS)染色检查，比较不同检查方法诊断的阳性率。结果：18例角膜后部真菌感染患者角膜刮片KOH涂片均未检查到真菌菌丝，其中有16例患者经活体共聚焦显微镜检查到真菌菌丝(88.9%)，而2例患者在术前活体共聚焦显微镜检查中未查到病原体。术后病理检查PAS染色联合GMS染色，18例患者中18例均可检查到真菌菌丝，角膜后部真菌感染患者病理切片中可见角膜深基质层变性坏死，大量炎症细胞浸润，PAS染色和GMS染色可见典型真菌菌丝侵犯角膜基质深层，而角膜基质浅层及上皮层均未查见真菌菌丝。结论：活体共聚焦显微镜诊断角膜后部真菌感染具有一定的局限性，联合术后病理组织切片和特殊染色检查有助于提高角膜后部真菌感染的诊断率。

Objective: To compare the diagnostic rate between in vivo confocal microscopy and pathological examination in retrocorneal fungal infection. Methods: It is a retrospective study. A total of 18 patients with retrocorneal fungal infection and received PKP surgery in the Qingdao Eye Hospital from November 2009 to December 2020 were enrolled. KOH smear and in vivo confocal microscopy examination were performed before surgery, and pathological examination including periodic acid-schiff (PAS) stain and Grocott Methenamine Silver (GMS) stain were performed after surgery. Patients were diagnosed retrocorneal fungal infection based on in vivo confocal microscopy and pathological examination. The diagnostic rates of the two methods were compared. Results: None of the 18 patients with posterior corneal fungal infection were found to have fungal hyphae in the corneal smear.Sixteen patients (88.9%) were found fungal hyphae by in vivo confocal microscopy. Corneal stroma necrosis and a large number of inflammatory cells were shown by postoperative pathologic examination, and all patients were found fungal hyphae in posterior corneal stroma with PAS stain and GMS stain. Conclusion: Confocal microscopy has unique advantages such as non-invasive and rapid examination in the diagnosis of fungal keratitis.However, it needs to combine with pathological examination for diagnosing the retrocorneal fungal infection.

目的：借助于人工智能(artificial intelligence,AI)眼底筛查远程接转诊系统，探索“患者-社区-医院”远程筛查模式，推进眼科分级诊疗和双向转诊实施，为地市级医疗机构开展眼底疾病人工智能筛查工作提供一定的经验借鉴。方法：通过AI辅助远程筛查基层医疗机构的4886例患者，完成眼科检查并经AI初判、人工复核形成眼底诊断结论。通过医联体和专科联盟模式，对基层医疗机构的4886例患者的AI诊断系统结果和上级医师审核结果进行对照分析，分析AI诊断系统在眼科常见病种筛查中的推广应用的可信度和可行性。结果：AI检出DR的灵敏度为94.70%，特异度96.06%；DME的灵敏度96.43%，特异度96.55%；AMD的灵敏度77.55%，特异度95.74%；同时，其在病理性近视、白内障、青光眼等常见病种眼底筛查中也有一定作用。结论：AI辅助远程筛查系统对于绝大多数眼底疾病有较高的敏感性和特异性，适用于眼底疾病的筛查工作，利于基层医院或社区医院对于眼底疾病的初步诊断，落实眼科分级诊疗，有借鉴推广意义。

Objective: With the help of artificial intelligence (AI) based fundus screening remote referral telemedicine system,it enables us to explore the remote screening mode of patient-community-hospital, and promote the two-way referral and ophthalmic graded diagnosis. This investigation provides certain practice experiences for prefecture-level medical institutions to carry out AI screening for fundus diseases. Methods: Ophthalmologic examination was performed on 4,886 patients in primary medical institutions through AI-aided remote screening, and the final fundus diagnosis conclusion was formed after AI preliminary judgment and manual review. Through the Medical Consortium and specialty alliance model, the results of the AI diagnosis system and the audit results of superior physicians for 4 886 patients in primary care institutions were compared and analyzed, and the credibility and feasibility of the AI diagnosis system application in the screening of common ophthalmic diseases were discussed. Results: The sensitivity and specificity of AI detection of diabetic retinopathy were 94.70% and 96.06%, respectively. In the diabetic macular edema classification, the sensitivity and specificity were 96.43% and 96.55%, respectively. In the age-related macular degeneration classification, the sensitivity and specificity were 77.55% and 95.74%, respectively. Meanwhile, it also plays a role in screening common fundus diseases such as pathological myopia, cataract and glaucoma. Conclusion: The AI-aided remote screening system has high sensitivity and specificity for most of fundus diseases, indicating it is promising for fundus diseases screening in primary medical institutions. It is conducive for primary hospitals or community hospitals to carry out the initial diagnosis of fundus diseases, as well as the implementation of graded diagnosis and treatment of ophthalmology, which has reference and promotion significance.