近年来，眼科人工智能(artificial intelligence，AI)迅猛发展，眼底影像因易获取及其丰富的生物信息成为研究热点，眼底影像的AI分析在眼底影像分析中的应用不断深入、拓展。目前，关于糖尿病性视网膜病变(diabetic retinopathy，DR)、年龄相关性黄斑变性(age-related macular degeneration，AMD)、青光眼等常见眼底疾病的临床筛查、诊断和预测已有较多AI研究，相关成果已逐步应用于临床实践。除眼科疾病以外，探究眼底特征与全身各种疾病之间的关系并据此研发AI诊断系统已经成为当下的又一热门研究领域。AI应用于眼底影像分析将改善医疗资源紧缺、诊断效率低下的情况，为多种疾病的筛查和诊断开辟“新赛道”。未来眼底影像AI分析的研究应着眼于多种眼底疾病的智能性、全面性诊断，对复杂性疾病进行综合性的辅助诊断；注重整合标准化、高质量的数据资源，提高算法性能、设计贴合临床的研究方案。

In recent years, artificial intelligence (AI) in ophthalmology has developed rapidly. Fundus image has become a research hotspot due to its easy access and rich biological information. The application of AI analysis in fundus image is under continuous development and exploration. At present, there have been many AI studies on clinical screening, diagnosis and prediction of common fundus diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma, and related achievements have been gradually applied in clinical practice. In addition to ophthalmic diseases, exploring the relationship between fundus features and various diseases and developing AI diagnostic systems based on this has become another popular research field. The application of AI in fundus image analysis will improve the shortage of medical resources and low diagnostic efficiency, and open up a “new track” for screening and diagnosis of various diseases. In the future, research on AI analysis of fundus image should focus on the intelligent and comprehensive diagnosis of multiple fundus diseases, and comprehensive auxiliary diagnosis of complex diseases, and lays emphasis on the integration of standardized and high-quality data resources, improve algorithm performance, and design clinically appropriate research program.

传统的眼底手术要求眼科医生具备精细的操作技术，但即便拥有再精湛的操作技术，眼底手术还是存在很大的风险性。因此，为了减少手术风险，提高手术质量，对传统眼底手术进行改进是十分必要的。近年来，在我国对于人工智能产业的大力支持之下，应用于各类行业的机器人随之诞生。机器人辅助系统(robot auxiliary system，RAS)在医学领域，特别是眼科学中应用广泛。对近几年RAS应用于眼底手术的案例进行整理总结，并将RAS参与的眼底手术以及传统的眼底手术进行对比，可以发现RAS在眼底手术中的应用可以显著提高手术效率，并降低手术风险。未来RAS的发展趋势可能着重聚焦于与深度学习算法的紧密结合。通过算法对手术中的视野图像进行预测、优化，从而让高精度的眼底手术更加高效、安全。

Traditional fundus surgery requires ophthalmologists to be equipped with sophisticated operating techniques, but even with the most sophisticated operating techniques, fundus surgery still has great risks. Therefore, in order to reduce the risk of surgery and improve the quality of surgery, it is very necessary to improve the traditional fundus surgery. In recent years, with China’s strong support for the artificial intelligence industry, robots used in various industries have been born. Robot auxiliary system (RAS) is widely used in the medical field, especially in ophthalmology. By summarizing the cases of fundus surgery with RAS in recent years and comparing the fundus surgery involving RAS with traditional fundus surgery, it can be found that the application of RAS in fundus surgery can significantly improve the efficiency of surgery and reduce the risk of surgery. The future development trend of RAS may focus on the close integration with deep learning algorithms, which can predict and optimize the field of view images during surgery so that high-precision fundus surgery can be more efficient and safer.

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.

目的：探索智能语音随访系统在医疗场景中的新型应用服务模式并分析其在新冠肺炎疫情期间的应用效果，以此评估该系统应用于互联网医院开展医疗咨询服务的实际效能。方法：本研究应用智能语音随访系统针对先天性白内障患儿术后的常见问题进行回访。首先，针对随访目的，设计出完善的结构化随访内容与步骤。其次，部署智能外呼系统自动拨打用户电话，并通过语音识别技术对用户的每次应答进行识别，根据用户的应答自动跳转到下一个随访步骤，在完成一系列问答后根据用户的回答给出恰当的建议，实现电话随访的自动化与智能化。收集2020年2月24日至2月28日期间，智能语音随访系统随访的电话内容、呼叫时间、患儿资料等数据，采用描述性统计分析。结果：2020年2月24日至2月28日期间，中山大学中山眼科中心应用智能语音随访系统电话共随访1154例，其中收到有效回访数据561例，平均有效回访率48.6%。有效回访人群中，有204位(36.4%)家属认为疫情期间复诊时间延长，对宝宝眼睛的恢复有影响，309位(55.1%)家属认为对宝宝眼睛的恢复没有影响。360位(64.2%)先天性白内障患儿眼睛恢复情况良好，没有出现不良反应，169位(30.1%)患儿出现不良反应和体征，包括瞳孔区有白点，眼睛发红和有眼屎流眼泪等。统计患儿不同行为显示，有417位(74.3%)患儿佩戴眼镜，135位(24.1%)患儿没有佩戴眼镜，另有9位(1.6%)患儿佩戴眼镜情况不清楚，经常揉眼的患儿更容易出现眼睛发红(20.4%)、眼睛有眼屎或流眼泪(17.0%)和瞳孔区有白点(6.8%)等不良反应。结论：智能语音随访系统在临床随访中显示出巨大的应用潜力，可作为一种新型的智能医疗服务模式。

Objective: This study was designed to explore its potential value for new medical service model based on the intelligent voice follow-up system and analyze its application effect during the outbreak of COVID-19. The actual effectiveness of this intelligent voice follow-up system applied in the Internet hospital to carry out medical consultation service was discussed. Methods: In this study, an intelligent voice follow up system was developed for postoperative follow-up of children with congenital cataract. First, a well-designed and structured questionnaire contents were developed for postoperative follow-up. Secondly, the intelligent voice follow-up system was deployed. The system would automatically jump to the next follow-up step according to the user’s response, and give appropriate suggestions. Finally, the data of telephone recording, call time, children’s attributes were collected and statistically analyzed. Results: From February 24 to March 15, 2020, 561 families of children with congenital cataract from Zhongshan Ophthalmic Center were recruited by using the intelligent voice follow-up system. The system completed a total of 1 154 calls, of which 561 cases received follow-up data, reaching an average effective call rate of 48.6%. Among 561 cases, 204 (36.4%) thought that the extended time of follow-up visit would affect the recovery of children, while 309 (55.1%) thought that it exerted no effect on the recovery. 360 children (64.2%) achieved good ocular recovery without complications, whereas 169 cases (30.1%) developed ocular symptoms. These include white spots in the pupil area, redness and eye secretions. Statistics of different behavior of children showed that there were 417 (74.3%) children wearing glasses, 135 (24.1%) children did not wear glasses, another 9 (1.6%) children wearing glasses were not clear, often rubbing the eyes of children were more likely to appear redness (20.4%), eye secretions (17.0%) and white spots in the pupil area (6.8%) and other adverse reactions. Conclusion: The intelligent voice follow-up system shows great application potential in clinical follow-up, which can be employed as a new service mode of intelligent medical treatment.

建立标准化的数据中心有利于收集高质量数据资源与促进医学人工智能的发展，在医疗大数据的基础上建立不同应用场景的医疗人工智能系统，整合、搭建可满足多种疾病诊疗需求的智能服务云平台，全面提升智能医疗管理的效率。本文以眼科为研究基础，对眼科数据中心和智能服务云平台的建设经验进行总结分析，为眼科及其他专科开展人工智能研究、建立数据中心、搭建智能服务云平台等方面提供参考。

The establishment of standardized data center can promote the accumulation of high-quality data resources and the development of medical artificial intelligence. On the basis of medical big data, medical artificial intelligence systems in different application scenarios can be established and integrated into an intelligent service cloud platform, which improves the management efficiency of intelligent medical systems. This article takes ophthalmology as a prototype to summarize the experience of the establishment of ophthalmic data center and intelligent service cloud platform, aiming to provide reference and guidance for ophthalmology and other specialties to carry out artificial intelligence research, establish data center and build an intelligent service cloud platform.