眼科人工智能技术在实践中不断发展，如大数据应用、图像信息分析、机器人时代等，现在又迈上促进生物识别精确化的新台阶，这些实践应用都能更好地保护视器官，使之具备正常视功能，展示出独特的视觉信息特色。眼科人工智能技术不断开辟新领域，取得了诸多新成就。

The application of artificial intelligence technology in ophthalmology has been on the agenda, and has continued to progress in practice, such as the application of big data, image information analysis, the era of robotics, and now it is on a new step to promote the accuracy of biometrics. These are protections for the visual organs and the vision, make they have normal visual function, and display unique characteristic of visual information. The “eye and artificial intelligence” has continuously opened up new fields and achieved new successes.

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.

目的：对视网膜光学相干断层扫描图像中不同层和积液区域的分割。方法：提出一种基于深度学习的轻量级的神经网络，参考DRUNet体系、膨胀卷积和残差网络的架构，通过连接不同深度网络处得到的上采样输出，进行多尺度特征融合，使网络能够更好地识别出图像中的边界信息。结果：改进型DRUNet显著提升了视网膜分层的效果，准确率较U-Net提高了1.25%，同时能提前1~2次迭代达到传统U-Net的准确度。结论：本文采用的网络结构提高了对视网膜光学相干断层扫描图像的分割性能，同时降低了网络参数，具有强大的应用潜力。

Objective: To achieve the segmentation of different layers and fluid areas on the optical coherence tomography (OCT) image of the retina. Methods: A lightweight neural network based on deep learning was proposed. The network structure adopted in this study was designed based on the architecture of dilated-residual U-Net. By connecting the upsampling output obtained at different depth networks, multi-scale feature fusion was performed to enable the system to accurately identify the boundaries on the OCT image. Results: Compared with U-Net, this algorithm could achieve the same accuracy with 1–2 epochs less, and the accuracy was also improved by 1.25%. Conclusion: The proposed network improves the segmentation performance of retinal OCT images, and reduces the number of parameters, which demonstrates the network has great application potential.

目的：针对活体共聚焦显微镜(in vivo confocal microscopy，IVCM)和传统光学相干层析技术(optical coherence tomography，OCT)在人眼角膜成像各自存在成像视野小或无法细胞成像的限制，开发具有高分辨率的非接触全视场光学相干层析系统(full-field optical coherence tomography，FFOCT)，实现活体人眼角膜细胞结构FFOCT成像。方法：FFOCT系统采用高数值孔径干燥显微物镜及高速面阵相机，使用双相位调制图像处理方法，实现系统高速高分辨率非接触成像。利用系统对健康人眼进行角膜各深度层的活体FFOCT成像验证其可行性。结果：本研究团队研发了FFOCT的新型活体人眼角膜高分辨率成像系统，实现理论平面成像分辨率1.7 μm，成像视野1.26 mm×1.26 mm，成像速率达275帧/s。利用该系统对正常活体人眼角膜成像实验，在非接触情况下获取了角膜各主要结构层的高分辨率结构影像。结论：FFOCT高分辨率活体人眼角膜成像系统兼具了传统OCT的非接触、大成像视野及IVCM的细胞级别平面分辨率的优势，将为角膜疾病的研究及临床诊疗提供全新的成像分析技术。

Objective: Due to the limitations of small imaging field of view of in vivo confocal microscopy (IVCM) or the incapability of cellular imaging of traditional optical coherence tomography (OCT) in human corneal imaging, this study was designed to develop a novel high-resolution in vivo human corneal imaging system based on full-field OCT (FFOCT). Methods: The FFOCT system utilized a high numerical aperture air immersion microscope objective and a high-speed area array CMOS camera with two-phase modulation image processing algorithm to achieve high-speed high-resolution non-contact imaging of human cornea. To verify its feasibility, in vivo cornea imaging at different depth was performed on a healthy human subject. Results: The FFOCT system achieved a theoretical lateral imaging resolution of 1.7 μm, an imaging field of view of 1.26 mm×1.26 mm, and an imaging rate of 275 Hz/s. High-resolution FFOCT images of the main structural layers of cornea were achieved by imaging a healthy human cornea in vivo with this system in a non-contact way. Conclusion: The FFOCT human corneal imaging system combines the advantages of the non-contractness and the large imaging field of view of traditional OCT with the cellular lateral resolution of IVCM, potentially providing a new imaging system for the research and clinical diagnosis and treatment of corneal diseases.

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：分析人眼的睑板腺形态学特征，探索睑板腺分析系统在眼表疾病的应用研究。方法：中山眼科中心入组正常受试者24例(42眼)，进行睑板腺红外摄影。选取受试者中的10例(20眼)在同型号的设备上由二名操作员分别进行睑板腺红外摄影。图像通过自行设计的分析软件对上睑结膜中央5条腺体形态学参数进行定量分析，对数据进行重复性测试。结果：测量的生物参数腺体直径为(0.48±0.09) mm，腺体长度为(5.25±0.68) mm，腺体面积为(2.12±0.53) mm，腺体形变系数为10.01±3.85，显影值为6.32±1.23，中央五条腺体占中央区域面积百分比为(10.94±2.20)%，腺体占上睑结膜面积百分比为(58.07±8.13)%。各指标两次测量值差异无统计学意义(P>0.05)。重复性分析结果显示：腺体各项生物参数的变异系数(coefficients of variation，CV)均小于5%，组内变异系数(intraclass correlation coefficient，ICC)均大于0.95。结论：睑板腺综合分析系统对腺体的形态学分析有良好的可靠性和一致性，有望为临床上对睑板腺腺体功能评估提供新的非侵入性参考指标。

Objective: To analyze the morphological characteristics of meibomian glands in human eyes and to explore the application research of meibomian glands analysis system in ocular surface diseases. Methods: A total of 24 healthy subjects were recruited by Zhongshan Ophthalmic Center to infrared photography of meibomian glands. Ten of healthy subjects were selected by the two operators for infrared photography of meibomian glands on the same model of equipment. The images were repeatedly measured and analyzed by the self-designed analysis software on the morphological measurements of the five glands in the center of the upper eyelid. Results: The measured biological parameters are shown below: the average gland diameter was (0.48±0.09) mm, the average gland length was (5.25±0.68) mm, the average gland area was (2.12±0.53) mm, the gland deformation coefficient was 10.01±3.85, the development value was 6.32±1.23, the percentage of the five central glands in the central area was (10.94±2.20)%, and the glands accounted for (58.07±8.13)% of the upper conjunctiva area. There was no statistical difference between the two measurements of each index (P>0.05). Repeatability analysis results showed that coefficients of variation (CV) of all biological parameters of glands were less than 5% and the intraclass correlation coefficient (ICC) in both groups were greater than 0.95. Conclusion: The Meibomian Gland Bioimage Analyzer provides good reliability and consistency for morphological measurements of the meibomian gland, and it is expected to provide new non-invasive indicators for clinical assessment of the meibomian glands function.

目的：获取眼表图像的综合信息，建立眼表疾病综合诊断和评估。方法：将超高分辨率光学相干断层成像仪(ultra-high resolution optical coherence tomography，UHR-OCT)与基于裂隙灯生物显微镜的微血管成像系统相结合，开发了一种多模态、非接触式的眼科光学成像平台。结果：UHR-OCT模块在组织中实现轴向分辨率约为2 μm 。眼表微血管成像模块在最大放大倍率下横向分辨率约为3.5 μm。通过集成在裂隙灯显微镜成像光学路径的不同模块，多模态成像平台能够执行实时前段OCT结构成像、结膜微血管成像和传统裂隙灯成像功能。利用自主开发的软件，进一步分析结膜血管网络图像和血流图像，获取血管分形维数、血流速度、血管直径等定量形态学和血流动力学参数。结论：通过在健康受试者和角膜炎患者的在体成像测试，表明多模态眼前段成像设备可为眼科临床应用及人工智能提供结构和功能信息数据。

Objective: To obtain the comprehensive information of the anterior eye image, establish complementary information for the diagnosis and evaluation of ocular diseases. Methods: We developed a multi-modal, non-invasive optical imaging platform by combining ultra-high resolution optical coherence tomography (UHR-OCT) with a microvascular imaging system based on slit-lamp biomicroscopy. Results: The uHR-OCT module achieved an axial resolution of approximately 2 μm in tissues. The lateral resolution of the ocular surface microvascular imaging module under maximum magnification was approximately 3.5 μm. By combining the imaging optical paths of different modules, the customized multi-modal eye imaging platform was capable of performing real-time cross-sectional UHR-OCT imaging of the anterior eye, conjunctival vessel network imaging, high-resolution conjunctival blood flow videography, and traditional slit-lamp imaging on a single device. With self-developed software, a conjunctival vessel network image and blood flow videography were further analyzed to acquire quantitative morphological and hemodynamics parameters, including vessel fractal dimensions, blood flow velocity and vessel diameters. Conclusion: The ability of the multi-modal anterior eye imager to provide both structural and functional information for ophthalmic clinical applications can be demonstrated in a healthy human subject and a keratitis patient.

当前，药物临床试验面临着两大难题：数据真实性及相关人员操作规范性。现阶段国内外在药物临床试验方面的监管主要以事后监查为主，在数据质量管理以及操作规划标准的监查方面存在一定的时延性。而区块链通过非对称加密、哈希算法及智能合约等技术，可以在保证受试者隐私信息的前提下，提高政府相关监督机构的监管效率，提升药物临床试验数据管理的透明度；同时，与物联网的紧密结合可以实现对标准操作规范的进一步核查，与人工智能的结合有望实现受试者的自动招募。

Clinical drug trials are confronted with two major issues: first, data authenticity, for instance, if any data falsification is conducted during the whole trial; second, whether the standard of procedure is accordingly conducted throughout the whole trial or not. Currently, both domestic and overseas clinical drug trials are not supervised without delay (ex-post inspection). Blockchain technology can improve the efficiency of Food and Drug Administration and the transparency of trials while the rights and safety of human research subjects are guaranteed by the integrated technology such as chained structure, asymmetry key algorithm, hash algorithm, and smart contract. Furthermore, with the assistance of internet of things (IoT) and artificial intelligence (AI), the actual supervision over the whole trial and automatic recruitment of human research subjects are expected to achieve.

    人工智能(artificial intelligence，AI)为解决中国患者“看病难”问题提供了可行方案。眼科AI已实现为患者提供筛查、远程诊断及治疗建议等方面的服务，能显著减轻医疗资源不足的压力和患者的经济负担。而AI的应用过程中，给医疗管理带来的挑战应引起重视。本文从医疗管理的角度，总结分析AI在眼科医疗过程中，尤其是交接环节中出现的主要问题，提出对策与建议，并讨论AI在眼科医疗的应用展望。

    Artificial intelligence (AI) has been proposed as a potential solution to address the shortage of ophthalmologists in China. With the increasingly extensive application of AI in the field of ophthalmology, many potential patients with eye diseases have access to a higher quality of medical services. At the same time, new challenges will emerge and proliferate with the advancement of AI application. This paper focuses on the patient handoffs process and discusses two challenges brought by the application of AI, namely “communication” and “standardization”. Natural language processing techniques and the development of standardized databases are proposed to solve each of these challenges. The application prospects of AI in ophthalmology are eventually discussed.

光学相干断层成像(optical coherence tomography，OCT)自1991年发明以来，在生物成像尤其在眼科和心血流成像中起越来越重要的作用。OCT的发展经历了早期的时域系统及最新的频域系统。其中频域系统又分为谱域OCT(spectral domain OCT，SD-OCT)系统和扫频OCT(swept source OCT，SS-OCT)系统。随着眼科临床应用对系统速度、灵敏度及功能化要求的不断提升，眼科扫频OCT已经走向成熟并逐步商用化。本文将简介扫频OCT的原理，并归纳扫频OCT相对于时域和谱域OCT系统的优势，并展示其在眼科临床的应用。

Optical coherence tomography (OCT) has played an important role in biomedical imaging, especially in ocular and cardiovascular imaging. OCT technology has evolved to frequency domain technology from early time-domain technology due to the advantages of high sensitivity and high speed of frequency domain techniques. The swept source OCT is a type of frequency domain OCT. With the increasing requirements for system speed, sensitivity, and functionality in clinical application, swept source OCT is gradually becoming commercially available and widespread in clinical application. In this paper, the principle of swept source OCT was introduced, the advantages of swept source OCT over time domain and spectral domain OCT systems were summarized, and its clinical application in ophthalmology was demonstrated.

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.

接触镜在全球的应用日益广泛，配适方法的不断进步是目前接触镜安全性、舒适性不断提高的原因之一。在接触镜适配过程中，越来越多的先进影像技术被运用于指导接触镜的配适，这些技术的出现简化了接触镜适配的过程，为临床医生进行简便、准确、个性化的接触镜适配提供了帮助，也为接触镜的个性化设计提供了参考数据。

Contact lens has been widely applied worldwide, and the advancement of fitting strategy is one of the reasons which improve the safety and comfort of contact lens fitting. During the contact lens fitting procedure, more and more ophthalmic imaging modalities have been applied to guide the contact lens fitting. These techniques simplify the contact lens fitting procedure, help optometrists accurately perform the customize contact lens fitting, and assist the personalized contact lens design technique.

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.

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.

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.

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.

本文根据上海鹰瞳医疗科技有限公司的创新产品《糖尿病视网膜病变眼底图像辅助诊断软件》在国家药品监督管理局(NMPA，原CFDA)历时两年半的上市前创新申报与注册申报经历，介绍了人工智能类医疗器械产品的产品研发、注册申报流程及相关重点难点，并且列明了在整个过程中需要遵循和参考的法律法规，为此类产品的上市前注册工作提供参考。

Based on the NMPA premarket application through two and a half years for the computer aided diagnosis software using fundus images of diabetic retinopathy, which is an innovative medical device of Shanghai EagleVision Medical Technology Co., Ltd. (Airdoc), this article introduced the development process, the premarket application, and the key points in the application of this artificial intelligence device, also lists the related regulations and guidelines as references to provide some ideas for the follow-up premarketing application of such kind of products.