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.
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.
在大力发展精准医疗的时代背景下，北京大学第三医院眼科中心率先建立干眼精准医疗平台。通过规范和优化干眼诊疗流程，为患者提供个性化的治疗方案和预防指导意见，有效提高了干眼诊断的精确性与治疗的有效性，同时提升了干眼门诊接诊效能，改善了患者就诊体验。本文将从干眼精准医疗平台体系的建设内容、标准化的检查流程、个性化的诊疗方案等方面进行阐述，并结合实际临床案例，综合分析北京大学第三医院在干眼精准医疗方面进行的探索，展望干眼精准医疗平台的前景与未来。
In the era of developing precision medicine, the Ophthalmic Center of Peking University Third Hospital has taken the lead in establishing a dry eye precision medical platform. By standardizing and optimizing the diagnosis and treatment process of dry eye, this center provides personalized treatment plan and prevention guidance for patients, effectively improves the accuracy of dry eye diagnosis and the effectiveness of treatment, at the same time,improves the reception efficiency of dry eye clinic, and improves the patient’s clinic experience. In this paper, the construction content, standardized inspection process and personalized diagnosis and treatment scheme of dry eye precision medicine platform system will be described. Combined with the actual clinical cases, the exploration of the Peking University Third Hospital in dry eye precision medicine will be comprehensively analyzed, and the future of dry eye precision medical platform will be prospected.
泪膜的不同组成成分通过相互作用共同维持眼球表面的湿润，从而维持眼部健康。当这些组成成分出现病理性改变，将会不同程度的影响泪膜稳态，从而导致干眼的发生。而瞬目运动一定程度上影响着泪膜组成成分的分布，随着对干眼相关机制研究的逐步深入，以泪膜为导向的诊断(tear-film-oriented diagnosis，TFOD)的新概念被提出，并被逐渐被接受。我们可以通过泪膜破裂方式来确定眼球表面所缺乏的组成成分，并在此基础上对干眼进行诊断，从而定向补充泪膜缺失成分，重新恢复泪膜稳态。本文将着重分析瞬目、泪膜形成及泪膜破裂机制之间的关系，从而进一步明确泪膜定向诊断的新概念及发展方向。
Different components of the tear film work together to maintain the wettability of the ocular surface, thus maintaining eye health. When the pathological changes of these components occur, the tear film homeostasis will be affected to varying degrees, leading to dry eye. Blinking movement affects the distribution of tear film components to some extent. With the continuous development of research and understanding of the concept and mechanism of dry eye, new concepts of tear-film-oriented diagnosis (TFOD) have been gradually proposed and widely accepted. We can determine the components lacking on the surface of the eye through the tear film breakup patterns (BUPs). On this basis, dry eye is diagnosed, so as to replenish the lacking components of tear film directionally and restore the stability of tear film. This paper will focus on analyzing the relationship between blinking, tear film formation and tear film break-up mechanism, so as to further clarify the new concept and development direction of tear-film oriented diagnosis.
目的：通过分析基于眼底彩照的人工智能（artificial intelligence,AI）在糖尿病视神经病变（diabetic optic neuropathy,DON）中的参数特征，探索AI在DON诊断中的应用价值。
方法：收集2020年1月1日至2022年4月30日就诊于东莞东华医院、横沥医院及东莞市寮步镇社区卫生服务中心并诊断为糖尿病的患者，采集其一般信息并拍摄以黄斑为中心、图片边缘距离视盘中心超过1PD的50°眼底彩照。眼底彩照由人工智能诊断系统分析获得视盘及血管检测参数，由3-4名眼底专家阅片后分为DON(+)、DON(-)两组并作糖尿病视网膜病变（diabetic retinopathy,DR）分期诊断。比较两组间视盘、血管检测参数的差异性，并分析各项参数以及DR分期与DON发病的相关性。
结果：研究共纳入糖尿病患者526人（945眼），其中男性335人，女性191人；平均年龄为51.58±12.21岁，平均病程为5.51±5.20年。所有入组病例中，DON(+)组205眼，DON(-)740眼；根据专科医师判读结果，无DR 723眼，轻度非增殖期糖尿病视网膜病变（non-proliferrative diabetic retinopathy,NPDR）7眼，中度NPDR 184眼，重度NPDR 24眼，增殖期糖尿病视网膜病变（proliferrative diabetic retinopathy,PDR）7眼。AI检测的视盘及血管参数中，水平视杯直径、垂直视杯直径、水平杯盘比、垂直杯盘比、B区视网膜静脉血管当量、B区视网膜动静脉比值在有或无DON组间存在显著差异；水平视盘直径、垂直视盘直径、弧形斑和视盘面积比、B区视网膜动脉当量在两组之间无显著差异。相关性分析发现，水平视杯直径、垂直视杯直径、水平杯盘比、垂直杯盘比、B区视网膜动静脉比值与DON患病呈负相关；B区视网膜静脉血管当量、DR分期则与其呈正相关。
结论：DON患者的视杯直径、杯盘比、B区视网膜静脉血管当量等基于眼底彩照的人工智能检测参数有显著改变；DON的发病与DR病变严重程度有关。
Objective: To explore the application value of artificial intelligence (AI) in the diagnosis of diabetic optic neuropathy (DON) by analyzing the parameter characteristics of artificial intelligence (AI) based on fundus color photos.
Methods: From January 1, 2020 to April 30, 2022, patients diagnosed with diabetes were collected in Dongguan Donghua Hospital, Hengli Hospital of Dongguan and Community Healthcare Center of Dongguan Liaobu. General information was collected and 50°field vision fundus images(centered on macula and the edge of the images were more than 1PD away from the center of the optic disc) were taken. All the images were divided into DON(+) and DON(-) groups by 3-4 ophthalmologists. All the parameters were detected and analyzed by AI system, and their differences between the two groups were compared. The correlation between each parameter and DR stage with the incidence of DON was analyzed as well.
Results: A total of 526 diabetic patients (945 eyes) were included in this study, including 335 males and 191 females. The mean age was 51.58±12.21 years, and the mean disease duration was 5.51±5.20 years. All the enrolled cases were divided into DON (+) group (205 eyes) and DON (-) group (740 eyes) . According to ophthalmologists’ interpretation, 723 eyes had no DR, 7 eyes had mild nonproliferrative diabetic retinopathy (NPDR), 184 eyes had moderate NPDR, 24 eyes had severe NPDR, 7 eyes had Proliferrative diabetic retinopathy (PDR). Among the parameters detected by AI, there were significant differences in horizontal and vertical optic cup diameter, horizontal and vertical C/D, retinal vein equivalent(RVE) in zone B, and retinal arteriole-to venule ratio(AVR) in zone B between DON(+) and DON(-) groups. There were no significant differences between the two groups in horizontal and vertical optic disc diameter, arc-shaped spot-to-disc area ratio, and retinal artery equivalent(RAE) in zone B. In the analysis of risk factors, horizontal and vertical optic cup diameter, horizontal and vertical C/D, and AVR in zone B were negatively correlated with the diagnosis of DON. RVE in zone B and the severity of DR were positively correlated with the diagnosis of DON.
Conclusions: The AI detection parameters based on fundus color photography have significant changes in the diameter of optic cup, C/D and RVE in zone B in DON patients. The incidence of DON is related to the severity of DR.
