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.

随着智能手机覆盖率的增加与可用性的提升，实现智能健康管理的应用程序成为新兴研究热点。新一代智能手机可通过追踪步数，监测心率、睡眠，拍摄照片等方式进行健康分析，成为新的医学辅助工具。随着深度学习技术在图像处理领域的不断进展，基于医学影像的智能诊断已在多个学科全面开花,有望彻底改变医院传统的眼科疾病诊疗模式。眼科疾病的常规诊断往往依赖于各种形式的图像，如裂隙灯生物显微镜、眼底成像、光学相干断层扫描等。因此，眼科成为医学人工智能发展最快的领域之一。将眼科人工智能诊疗系统部署在智能手机上，有望提高疾病诊断效率和筛查覆盖率，改善医疗资源紧张的现状，具有极大的发展前景。综述的重点是基于深度学习和智能手机的眼病预防与远程诊疗的进展，以糖尿病性视网膜病变、青光眼、白内障3种疾病为例，讲述深度学习和智能手机在眼病管理方面的具体研究、应用和展望。

With the increasing coverage and availability of smart phones, the application of realizing intelligent health management has become an emerging research hotspot. The new generation of smart phones can perform health analysis by tracking the step numbers, monitoring heart rate and sleep quality, taking photos and other approaches, thereby becoming a new medical aid tool. With the continuous development of deep learning technology in the field of image processing, intelligent diagnosis based on medical imaging has blossomed in many disciplines, which is expected to completely change the traditional eye diseases diagnosis and treatment mode of hospitals. The conventional diagnosis of ophthalmic diseases often relies on various forms of images, such as slit lamp biological microscope, fundus imaging, optical coherence tomography, etc. As a result, ophthalmology has become one of the fastest growing areas of medical artificial intelligence (AI). The deployment of ophthalmological AI diagnosis and treatment system on smart phones is expected to improve the diagnostic efficiency and screening coverage to relieve the strain of medical resources, which has a great development prospect. This review focuses on the prevention and telemedicine progress of eye diseases based on deep learning and smart phones, taking diabetic retinopathy, glaucoma and cataract as examples to describe the specific research, application and prospect of deep learning and smart phones in the management of eye diseases.

近年来，使用人工智能(artificial intelligence，AI)技术对临床大数据及图像进行分析，对疾病做出智能诊断、预测并提出诊疗决策，AI正逐步成为辅助临床及科研的先进技术。生物样本库作为收集临床信息和样本供科研使用的平台，是临床与科研的桥梁，也是临床信息与科研数据的集成平台。影响生物样本库使用效率及合理共享的因素有信息化建设水平不均衡、获取的临床及检验信息不完全、各库之间信息不对称等。本文对AI和区块链技术在生物样本库建设中的具体应用场景进行探讨，展望大数据时代智能生物样本库信息化建设的核心方向。

In recent years, artificial intelligence (AI) technology has been applied to analyze clinical big data and images and then make intelligent diagnosis, prediction and treatment decisions. It is gradually becoming an advanced technology to assist clinical and scientific research. Biobank is a platform for collecting clinical information and samples for scientific research, serving as a bridge between clinical and scientific research. It is also an integrated platform of clinical information and scientific research data. However, there are some challenges. First, clinical and laboratory information obtained is incomplete. Additionally, the information among different databases is asymmetric, which seriously impedes the information sharing among different Biobanks. In this article, the specific application scenarios of AI technology and blockchain in the construction of a Biobank were discussed, aiming to pinpoint the core direction of the information construction of an intelligent Biobank in the era of big data.

目的：探讨上睑提肌缩短术和额肌肌瓣悬吊术后眼表改变和恢复的差异。

方法：对2007 年1 月至2007 年4 月在中山眼科中心住院的42例(62只眼)先天性上睑下垂患者，按手术方式和术后是否加用局部用药进行随机分组，观测各组术后2 d、5 d、7 d和2周患者泪液的分泌、泪膜破裂时间、结膜充血、角膜荧光染色、睑板腺功能、瞬目次数、上睑睫毛角度和眼睑闭合情况，并分析其观察结果的差异是否有统计学意义。

结果：3名患者(7.1 ％)因需要加用其它促角膜上皮生长的药物而退出本研究，其中1例(2.3 ％)因倒睫刺激角膜上皮水肿缺损需行手术调整，其余所有患者眼表检测项目的结果均显示不同程度地受到了手术影响，但是随着术后炎症的逐渐消退，这些受影响的眼表异常均会逐渐恢复正常。泪膜破裂时间、瞬目次数、眼睑闭合情况的影响在两种术式之间的差异有统计学意义，而在术后是否局部用药之间没有统计学差异；角膜荧光素染色在是否加用局部用药组之间有统计学差异，而不同术式之间没有统计学差异；泪液分泌量、结膜充血、睑板腺功能、睫毛角度则在所有组别之间均没有统计学差异。

结论：两种上睑下垂的矫正术均会引起患者眼表的改变，额肌肌瓣悬吊术对泪膜破裂时间、瞬目次数、眼睑闭合情况影响的程度较大，而局部用药只能改善角膜荧光素染色异常、对其它眼表因素影响不大。上睑睫毛角度异常是引起角膜损害最危险的因素。

Objective : To investigate the difference of ocular surface change and restoration after ex-ternal levator advancement and frontalis suspension.

Methods : Forty-two patients (62 eyes) with congenital ptosis hospitalized in ZhongshanOphthalmic Center from Jaruary to April in 2007 were randomly divided into four groups according to different surgery types and with or without post surgery ophthalmic medica-tion. Sehirmer test, tear film break-up time , conjunctiva congestion , cornea fluorescentpigmentation , tarsal gland function , winking frequency, angle of eyelash and eyelid clo-sure were all observed and statistically analyzed in all groups 2 days , 5 days , 7 days and 2 weeks after surgery.

Results : Except 3 patients needed advanced ophthalmic medicine, one of whom waswith corneal ulceration and needed another surgery, all the others were observed withocular surface items altered in varied degrees and gradually returned to normality as theinflammation caused by surgery recovered. Break-up time , winking frequency and eyelid closure were statistically diferent between the two types of surgery but not betweengroups with and without post surgery ophthalmic medication. Cornea fluorescent pigmen-tation was statistically different between groups with and without post surgery ophthalmicmedication but not between the two types of surgery. The other items did not have statis-tical difference in all groups.

Conclusion : The two types of surgery for ptosis correction could alter the ocular surface ,but frontalis suspension affect tear film break-up time , winking frequency and eyelid clo-sure much more than levator advancement. Ophthalmic medication after the surgerycould only ameliorate cornea fluorescent pigmentation but was not necessarily to restora-tion of normal ocular surface. Abnormality of eyelash angle was the most dangerous fac-tor to the corea injury.

      调节是人眼非常重要的功能，通过调节能随时改变人眼屈光系统的光学参数，与眼屈光不正及老视都有着密切的关系。测量眼调节力的常用方法分为主观测量法和客观测量法。主观测量法以移近移远法、负镜片法为代表。客观测量法以动态视网膜检影法和自动屈光仪法为代表。本文就调节力测量方法、测量准确度和调节力的最新研究进展进行综述，为眼科临床研究和应用提供选择依据。

Accommodation is an important function of the human eye, which can change the parameters of ocular refractive system and also has a strong correlation with the development of myopia and presbyopia. Several subjective measurements have been applied in accommodation assessment such as push-up test, push-down test and minus-lens procedures. It can be measured objectively by measuring the change in refraction of the eye with dynamic retinoscopy or autorefractor. This article reviews the application of measurement of accommodative amplitude and research progress in accommodation, providing clinical information for further studies．

目的：住院医师委托培养是我国医学教育标准化和国际化的重要举措。本研究采用客观考核和主观问卷两种方法评估我国眼科住院医师委托培养效果。方法：本研究对象为广东省深圳市政府于

2012年8月至2015年7月期间委托中山大学中山眼科中心进行眼科住院医师规范化培训的9名学员。本研究对所有学员的基本信息、临床培训情况、以及考核成绩等客观指标进行统计分析，同时设 计了一份包含13个问题的调查问卷，对每位学员的培训情况进行主观评估。结果：本研究纳入9名研究对象，包括2名男性和7名女性，平均年龄为(26±3)岁，学历水平情况为学士7名和硕士2名，其中有7名毕业于国家重点医学院校。3年内人均轮转眼科亚专科超过10科，平均主管病人数及参加手术例数分别为736和1 219例，门诊工作总量人均6 274人次，所有学员均按规定至少完成综 述1篇。8名(88.89%)学员认为可以独立诊断并治疗大部分常见眼科疾病，而且能独立完成大部分眼科基本临床操作，5名(55.56%)学员可以单独完成翼状胬肉切除术、霰粒肿刮除术、前房穿刺术等。6名(66.67%)学员认为培训时间安排合理。8名(88.89%)学员对这次委托培养总体比较满意。 所有9名培训学员中，最终有7名(77.8%)顺利通过中山大学第一阶段住院医师规范化培训考核。结论：培训学员对现行的眼科住院医师规范化培训方案的接受程度较高，基本达到预期的培训效果，对常见眼病能独立进行诊治。委托培养学员的学历和学习能力的差异在一定程度上影响了最终的培训考核通过率。

Objective: The entrusted standardized training for residents is an important measure to gear the medical education in China to the international conventions. In this study, the effectiveness of standardized training for entrusted ophthalmic residents in China was evaluated both objectively and subjectively. Methods: Nine ophthalmic residents, commissioned by Shenzhen government of Guangdong Province, studied at Zhongshan Ophthalmic Center, Sun Yat-sen University during August 2012 to July 2015 were included in this study. The objective indicators of all participants were analyzed, including the basic information, clinical training, the score of examination, etc. The subjective self-assessment was also implemented thought a questionnaire including 13 designed questions.Results: All 9 participants included 2 males and 7 females, 2 medical masters and 7 bachelors, and the mean age was 26±3 years. Seven of them graduated from the national key medical universities. The mean number of rotated sub-clinical departments was 10.3, the mean number of managed inpatients and the participated operations were 736 and 1,219, respectively. The total number of managed outpatients was 6,274 in average. All participants completed at least one review article. Eight (88.89%) participants could independently diagnose and treat the most common ophthalmic diseases, they also could complete the basic clinical ophthalmic operation independently. Five (55.56%) participants could independently manage the pterygium excision, curettage of chalazion, anterior chamber penetration, etc. And 6 (66.67%) of the participants believed that the training length was reasonable. Eight (88.89%) of them were satisfied with the standardized training for residents on the whole. Finally, 7 participants successfully passed the first stage of standardized training program in Sun Yat-sen University. Conclusion: There was a high level acceptance rate of the standardized training programs for entrusted ophthalmic residents. The participants achieved the expected training effects, and could managed the diagnosis and treatment of common ophthalmic diseases independently. But the training effects and passing rate of examination were partly affected by the learning ability of the training students.

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.

目的：评估白内障人工智能辅助诊断系统在社区筛查中的应用效果。方法：采用前瞻性观察性研究方法对白内障人工辅助诊断系统的应用效果进行分析，结合远程医疗的模式，由社区卫生人员对居民进行病史采集、视力检查和裂隙灯眼前节检查等，将数据上传至云平台，由白内障人工智能辅助诊断系统和人类医生依次进行白内障评估。结果：受检人群中男性所占比例为35.7%，年龄中位数为66岁，裂隙灯眼前节照片有98.7%的图像质量合格。该白内障人工智能辅助诊断系统在外部验证集中检出重度白内障的曲线下面积为0.915。在人类医生建议转诊的病例中，有80.3%也由人工智能系统给出了相同的建议。结论：该白内障人工智能辅助诊断系统在白内障社区筛查的应用中具有较好的可行性和准确性，为开展社区筛查疾病提供了参考依据。

Objective: To evaluate the effectiveness of an artificial intelligence-assisted diagnostic system for cataract screening in community. Methods: A prospective observational study was carried out based on a telemedicine platform. Patient history, medical records and anterior ocular segment images were collected and transmitted from community healthcare centers to Zhongshan Ophthalmic Center for evaluation by both ophthalmologists and artificial intelligence-assisted cataract diagnostic system. Results: Of all enumerated subjects, 35.7% were male and the median age was 66 years old. Of all enumerated slit-lamp images, 98.7% met the requirement of acceptable quality. This artificial intelligence-assisted diagnostic system achieved an AUC of 0.915 for detection of severe cataracts in the external validation dataset. For subjects who were advised to be referred to tertiary hospitals by doctors, 80.3% of them received the same suggestion from this artificial intelligence-assisted diagnostic system.Conclusion: This artificial intelligence-assisted cataract diagnostic system showed high applicability and accuracy in community-based cataract screening and could be a potential model of care in community-based disease screening.

近年来随着人类生活方式的改变、用眼频率的增加，眼科药物的市场需求持续增长，但是目前眼病治疗仍面临“缺医少药”的困境。由于新药研发面临成本高、周期长、成功率低的风险，眼科药物创新迭代的进程日趋缓慢。人工智能(artificial intelligence，AI)作为一种全新的技术手段，有望赋能眼科药物研发的全过程，包括药物靶点发现、化合物筛选、药物动力学模型创新与临床试验开展等，以期为眼科药物研发“降本增效”。且随着大数据体系的完善、硬件计算力的提升以及生命科学与智能科学的深度融合，AI在眼科药物研发中的作用将进一步得到提升，助力眼科药物研发实现从精准化到智能化的跨越。

With the change of human lifestyle and overuse of eyes in recent years, the market demand for ophthalmic drugs continues to grow. However, the ocular therapy is still facing the shortage of doctors and drugs. Due to the risk of high cost, long lead time and low success rate, the process of novel ophthalmic drug innovation and iteration is getting slower. As an emerging technology, artificial intelligence is expected to enable the whole process of ophthalmic drug discovery and development, including drug target discovery, compound screening, pharmacokinetic model innovation and clinical trials, thus reducing R&D costs and increase efficiency for ophthalmic drug discovery and development. In addition, with the improvement of big data, hardware calculation and the deep integration of life science and intelligent science, the role of artificial intelligence in ophthalmic drug discovery and development will be significant improved , contributing to achieve the leap from precision to intelligence.

手术前常规检查在临床诊疗中被广泛应用，但在一些低风险择期手术前对患者进行常规检查，对提高医疗质量并无帮助，反而降低了医疗效率，增加了医疗费用。为提高效率，一些地区、机构和专家学者陆续通过宣传教育、发表共识、制定指南等方式控制无指征术前常规检查，但效果仍依赖于执业者的重视程度和专业水平。大数据机器学习方法以其标准化、自动化的特点为解决这一问题提供了新的思路。在回顾已有研究的基础上，我们抽取2017至2019年在中山大学中山眼科中心进行眼科手术的3.4万名患者的病史和体格检查资料大数据，涵盖年龄、性别等口学信息，诊断、既往疾病等病史信息，视功能、入院时身体质量指数(BMI)等体格检查信息。并以此为基础使用机器学习方法预测术前胸部X线检查是否存在异常，受试者操作特性曲线(receiver operating characteristic curve，ROC)曲线下面积达到0.864，预测准确率可达到81.2%，对大数据机器学习精简术前常规检查的新方式进行了先期探索。

Preoperative routine tests are widely prescribed in clinical settings. However, these tests do not help improving the quality of medical care in low-risk elective surgery. Instead, they are associated with lower efficiency and increasing fees. To improve the efficiency, many regions, institutions, and scholars have attempted to reduce preoperative routine tests without indications through propaganda, education, consensus, and guidelines. Nevertheless, the effects are still highly dependent on the expertise and emphasis of practitioners. Machine learning based on big data provide a new solution with its standardization and automation. Through literature review, we extracted the big data, including demographic features such as sex and age, histories including diagnosis and chronic diseases, and physical examination features such as visual function and body mass index. A total of 34 000 patients undergone ocular surgeries in Zhongshan Ophthalmic Center, Sun Yat-sen university from 2017 to 2019. Machine learning was adopted to predict the risk of finding abnormalities in chest X-ray examination, with an accuracy of 81.2%. Area under the Receiver Operating Characteristic curve was 0.864. The study could be an early exploration into the field of simplifying preoperative tests by machine learning.