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：分析某三甲眼科专科医院近5年临床诊疗涉及非手术常规血液检验项目的申请检测情况，为眼科医师了解检验辅助诊断概况、专科医院的实验室项目管理和开展新项目提供依据。方法：从中山大学中山眼科中心医学检验信息管理系统导出2018年1月1日至2022年12月31日期间到院申请进行血液检测的12 866例门诊患者的22 453份样本(共94 081项检验项目)相关检验记录。将申请检测科室及专科医师按照中华医学会眼科学分会推荐分为10个亚专科，对疾病诊断和检测项目等资料进行统计分析，采用文字、柱状图及折线图的形式进行描述。结果：5年间申请进行血液检测的12 866例门诊患者，男性患者6 356例(49.4%)，女性患者6 510例(50.6%)。基于首诊眼病诊断分类，排名前三位的眼病分别为眼整形/眼肿瘤病5 214例 (40.5%)、眼底病 3 487例(27.1%)、角膜病1 711例(13.3%)。申请检测样本量从2018年的3 163份增至2022年的5 903份，总体呈上升趋势。从申请的专科医师分析，眼整形/眼眶病专科医师申请单最多，有6 751份(占30.1%)，其中自身免疫性疾病检测所占比例最高，为49.1%，甲状腺疾病相关检测占41.9%；眼免疫专科医师申请检测量为4 214份(占18.8%)，以自身免疫性疾病检测为主占55.7%，感染性项目检测占32.5%；眼底病专科医师申请检测量为3 629份(16.2%)，其中自身免疫性疾病检测和感染性项目检测分别占47.8%和39.6%；角膜病专科医师申请数为1 436份(占6.4%)，其中过敏性疾病检测比例为41.2%。基于一份申请单可同时检测多个项目，眼整形/眼眶专科申请检测总项最多，有33 513项，甲状腺疾病检测占65.0%；角膜病专科申请16 482项，过敏性疾病检测占83.4%，眼底病专科和眼免疫专科分别为8 794项和8 047项，均以自身免疫性疾病检测为主，分别占42.5%和40.4%。结论：眼科专科医院中非手术常规检验项目在各亚专科的申请数量明显分布不均，项目构成受亚专科疾病特点的影响。眼科实验室应针对性加强非手术常规检测项目的宣传和管理。
Objective: To analyze the application and testing of non-surgical routine blood test items in clinical diagnosis and treatment in a top-tier ophthalmic hospital over the past five years, providing ophthalmologists with insights into the overview of laboratory-assisted diagnosis, laboratory project management in specialized hospitals, and the basis for launching new projects. Methods: Relevant test records of 22,453 samples (totaling 94,081 test items) from 12,866 outpatient patients who applied for blood tests at the Zhongshan Ophthalmic Center of Sun Yat-sen University from January 1, 2018, to December 31, 2022, were retrieved from the medical laboratory information management system. The departments applying for tests and specialist physicians were divided into 10 subspecialties according to the recommendations of the Ophthalmology Branch of the Chinese Medical Association. Statistical analysis was performed on disease diagnosis and test items, and the results were described in the form of text, bar graphs, and line graphs. Results: Among the 12,866 outpatient patients who applied for blood tests over the five-year period, 6,356 (49.4%) were male and 6,510 (50.6%) were female. Based on the classification of first-visit ophthalmic diseases, the top three were ophthalmic plastic surgery/ocular tumor diseases (5,214 cases, 40.5%), fundus diseases (3,487 cases, 27.1%), and corneal diseases (1,711 cases, 13.3%). The number of samples applied for testing increased from 3,163 in 2018 to 5,903 in 2022, showing an overall upward trend. From the perspective of specialist physicians applying for tests, ophthalmic plastic surgery/orbital disease specialists had the highest number of applications, with 6,751 (30.1%), among which autoimmune disease testing accounted for the highest proportion, at 49.1%, and thyroid disease-related testing accounted for 41.9%. The number of applications from ophthalmic immunology specialists was 4,214 (18.8%), with autoimmune disease testing accounting for 55.7% and infectious disease testing accounting for 32.5%. The number of applications from fundus disease specialists was 3,629 (16.2%), with autoimmune disease testing and infectious disease testing accounting for 47.8% and 39.6%, respectively. The number of applications from corneal disease specialists was 1,436 (6.4%), with allergic disease testing accounting for 41.2%. Since multiple tests could be performed on a single application, the ophthalmic plastic surgery/orbital disease subspecialty had the highest total number of tests applied for, with 33,513 tests, of which thyroid disease testing accounted for 65.0%. The corneal disease subspecialty applied for 16,482 tests, with allergic disease testing accounting for 83.4%. The fundus disease subspecialty and ophthalmic immunology subspecialty respectively applied for 8,794 and 8,047 tests, both primarily focused on autoimmune disease testing, accounting for 42.5% and 40.4%, respectively. Conclusions: The number of applications for non-surgical routine test items in ophthalmic specialized hospitals is significantly unevenly distributed among various subspecialties, and the composition of the items is influenced by the characteristics of diseases in each subspecialty. Ophthalmic laboratories should strengthen the promotion and management of non-surgical routine test items in a targeted manner.
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.
碳点是一种新型荧光碳纳米材料，直径一般小于10 nm，具有自发荧光、高生物组织相容性、易于修饰、成本低廉等优点，在生物医学领域拥有广阔的应用前景。眼球因其独特的屏障结构，常规药物停留时间短、穿透性差，通过局部滴眼到达病灶的药物浓度有限，需要增加给药频次以保持药效。另外，糖尿病性黄斑水肿(diabetic macular edema,DME)、脉络膜新生血管(diabetic macular edema,CNV)等疾病的治疗给药则需依赖于玻璃体腔注射，该方法属于有创操作，有引起潜在并发症的可能，且需多次注射，给患者造成了沉重的心理和经济负担。优化眼部给药方法一直是眼科学领域的研究热点。基于碳点的优异特性，碳点在眼部药物递送、眼部成像、眼疾病诊疗中已展现出优秀的应用潜力。本综述将综合介绍碳点的特点及近十年来碳点在眼科疾病诊疗中的研究进展，旨在提供关于碳点在眼科应用现状的系统性认识，为未来研究提供方向。
Carbon dots is a new type of fluorescent carbon nanomaterial, which the diameter is generally less than 10 nm, has the advantages of self-fluorescence, remarkable biocompatibility, easy modification, low cost and so on, has a broad application prospect in the biomedical field. Due to the unique barrier of the eye, conventional drugs have a short residence time and poor penetration, so the concentration of drugs that can reach the lesions through local eye drops is limited, and for what to increase the frequency of administration to maintain efficacy. Up to now, the treatment of posterior eye diseases, such as diabetic macular edema (DME), choroidal neovascularization (CNV) and other diseases still rely on repeated vitreous injection, which is an invasive procedure with potential complications, and need multiple injections, causing a heavy psychological and economic burden on patients. Optimizing the method of ocular drug delivery has always been a hot topic in the field of ophthalmology. Carbon dots have shown excellent application potential in the ocular drug delivery, ocular imaging, and diagnosis and treatment of ocular disease based on its excellent characteristics. This review will systematically introduce the characteristics of carbon dots and the application of carbon dots in the diagnosis and treatment of eye diseases, aiming to provide a comprehensive understanding of the current situation of the application of carbon dots in ophthalmology and provide directions for future research.
随着人工智能(artificial intelligence,AI)技术的快速发展，其在医疗领域的应用正带来革命性的变化。白内障作为全球范围内最常见的可逆性视力障碍之一，在管理和治疗方面依然存在着医疗资源不足、诊断精度低、转诊效率低等诸多实际问题。因此，利用AI技术强大的计算分析和智能决策能力，优化传统医疗实践方式，对于保障人们的视觉健康至关重要。该文探讨AI技术在推动白内障分级诊疗新模式方面的应用，包括白内障图像自动分析与识别、远程医疗和转诊支持等，这些应用能够为白内障患者、社会以及政府带来多方面的显著益处和重要影响，有助于提高白内障诊断和治疗效率，缓解医疗资源不均衡问题，优化医疗资源的配置和管理，推动社会健康进步。然而，AI技术的实际应用也面临风险和挑战，应当充分重视和保护患者数据隐私和安全，建立严格的监管和监督机制，并持续加强技术创新，全面评估AI算法的鲁棒性、公平性和可解释性，以进一步提高AI系统的准确度和可信度。
With the rapid development of artificial intelligence (AI) technology, its application in the field of healthcare is bringing revolutionary changes. Cataracts, as one of the most common reversible visual impairments worldwide, still face many practical issues in terms of limited medical resources, low diagnostic accuracy, and low referral efficiency. Therefore, it is crucial to utilize AI technology's powerful computational analysis and intelligent decision-making capabilities to optimize traditional medical practices and safeguard people's visual health.This article investigates the applications of AI technology on a new model of hierarchic diagnosis and treatment for cataracts, including automatic analysis and recognition of cataract images, remote healthcare, and referral support. These applications can bring significant benefits and important impacts to cataract patients, society, and governments. They can help improve the efficiency of cataract diagnosis and treatment, alleviate the imbalance of medical resources, optimize the allocation and management of healthcare resources, and promote societal health progress.However, the practical application of AI technology also faces risks and challenges. It is important to fully prioritize and protect patients' data privacy and security by establishing strict regulatory and oversight mechanisms. Additionally, continuous efforts should be made to enhance technological innovation and comprehensively evaluate the robustness, fairness, and interpretability of AI algorithms to further improve the accuracy and trustworthiness of AI systems.
人工智能(artificial intelligence，AI)在眼科领域的应用不断深入、拓展，目前在糖尿病性视网膜病变、白内障、青光眼以及早产儿视网膜病变在内的多种常见眼病的诊疗中逐渐成为研究热点。AI使医疗资源短缺、诊断标准缺乏、诊疗技术水平低下的现状得到改善，为白内障的诊疗开辟了一条“新赛道”。本文旨在综述AI在白内障诊疗中的应用现状、进展及局限性，为AI在白内障领域的进一步开发、应用及推广提供更多信息。
Artificial intelligence (AI) has been widely applied and promoted in ophthalmology, and has gradually become a research hotspot in the diagnosis and treatment of many common ophthalmopathies, including diabetic retinopathy, cataract, glaucoma, and retinopathy of prematurity. AI improves the shortage of medical care, the lack of diagnostic criteria and the low level of diagnosis and treatment technology, and explores a “new race track” for cataract diagnosis and treatment. The purpose of this article is to review the application status, progress and limitations of AI in the diagnosis and treatment of cataract, aiming to provide more information for further development, application and promotion of AI in the field of cataract.
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.
随着移植技术逐年发展，异基因造血干细胞移植患者的生存期延长，长期并发症成为影响患者预后及生活质量的主要原因。眼移植物抗宿主病是异基因造血干细胞移植术后最常见的眼部并发症，发生率可高达50%以上。根据发病时间可分为急性及慢性眼移植物抗宿主病，临床上最常以慢性炎症及眼表组织纤维化为特点，主要表现为干眼和不同程度的角结膜炎，治疗较为棘手，可不同程度影响患者视觉质量及生活质量，严重可致盲。近年来眼移植物抗宿主病越来越受到国内外学者重视，其发病机制、临床特点、诊断及治疗相关研究逐渐深入，文章针对眼移植物抗宿主病的临床诊疗新进展进行综述。总体而言，眼移植物抗宿主病早期识别仍较为困难，早期诊断策略有待进一步探索。目前治疗对眼移植物抗宿主病的效果较为有限，或缺乏充足的循证医学证据，临床上缺乏针对不同严重程度及疾病活动度的分级诊疗策略，未来有待进一步探索新的治疗靶点及疾病活动监测指标，将有助于改善患者长期预后及生活质量。
Despite advancements in allogeneic hematopoietic stem cell transplantation techniques leading to improved overall survival rates, long-term complications have emerged as the primary contributors to poor prognosis and diminished quality of life. Ocular graft-versus-host disease (oGVHD), a prevalent complication affecting over 50% of patients post-transplantation, frequently manifests as refractory dry eye, often accompanied by keratoconjunctivitis. Patients with oGVHD routinely suffer from visual impairment and a decline in their quality of life.Currently, research into the mechanisms, clinical features, diagnosis, and treatment of oGVHD has progressively deepened. This article reviews the latest advancements in the clinical diagnosis and management of oGVHD. Notably, there is a pressing need for strategies focused on early diagnosis and treatment, as early recognition of oGVHD remains challenging. Existing treatments for oGVHD either exhibit limited efficacy or lack robust clinical evidence to support their use as the best available options.Further research is imperative to develop tiered diagnostic and treatment approaches, including the exploration of novel therapeutic targets and biomarkers for disease detection. Such endeavors hold the promise of enhancing patients' long-term prognosis and quality of life.
