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.
目的：建立和验证一个涉及多级临床场景的白内障协作通用的人工智能(artificial intelligence,AI)管理平台，探索基于AI的医疗转诊模式，以提高协作效率和资源覆盖率。方法：训练和验证的数据集来自中国AI医学联盟，涵盖多级医疗机构和采集模式。使用三步策略对数据集进行标记: 1)识别采集模式；2)白内障诊断包括正常晶体眼、白内障眼或白内障术后眼；3)从病因和严重程度检测需转诊的白内障患者。此外，将白内障AI系统与真实世界中的居家自我监测、初级医疗保健机构和专科医院等多级转诊模式相结合。结果：通用AI平台和多级协作模式在三步任务中表现出可靠的诊断性能: 1)识别采集模式的受试者操作特征(receiver operating characteristic curve,ROC)曲线下面积(area under the curve,AUC)为99.28%~99.71%)；2)白内障诊断对正常晶体眼、白内障或术后眼，在散瞳-裂隙灯模式下的AUC分别为99.82%、99.96%和99.93%，其他采集模式的AUC均 > 99%；3)需转诊白内障的检测(在所有测试中AUC >91%)。在真实世界的三级转诊模式中，该系统建议30.3%的人转诊，与传统模式相比，眼科医生与人群服务比率大幅提高了10.2倍。结论：通用AI平台和多级协作模式显示了准确的白内障诊断性能和有效的白内障转诊服务。建议AI的医疗转诊模式扩展应用到其他常见疾病和资源密集型情景当中。
Objective: To establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage. Methods: The training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three step strategy: (1)capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services. Results: The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3)detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be ’referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern. Conclusions: The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.
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.
当下，我国眼科的发展存在失衡现象，大城市与农村及偏远地区在眼科相关诊疗设施水平、诊疗技术等方面存在巨大差异，仍需探寻新的智能诊疗模式以解决失衡问题。由于眼球是唯一可以直接观察人体血管和神经的器官，眼部可反映其他脏器的健康状态，部分眼科检查的医学图像可对眼部疾病做出诊断等特点，眼科开展人工智能(artificial intelligence，AI)具有独到的优势。此外，人工智能可在一定程度上提高跨时间空间传递信息的精准度及效率。人工智能在眼科及远程信息传递的优势为解决眼科发展失衡状况提供了助力。本文从眼科人工智能在眼科远程医疗相关应用的角度，主要分析并总结当下我国人工智能在眼科相关疾病远程医疗中的发展程度、所具优势以及存在问题，并讨论眼科人工智能在远程医疗的应用展望。
At present, there is an imbalance in the development of ophthalmology in China. There are huge differences in the level of ophthalmology related facilities, diagnosis and treatment technologies between big cities and rural, remote areas. New intelligent diagnosis and treatment models are still needed to solve the imbalance. Since the eye is the only organ that can directly observe the blood vessels and nerves of the human body, the eye can reflect the health status of other organs and diagnosis of eye diseases based on medical images of some ophthalmic examinations can be made as well as other characteristics. Therefore, the development of artificial intelligence in ophthalmology has unique advantages. In addition, artificial intelligence can improve the accuracy and efficiency of information transmission across time and space to a certain extent. The advantages of artificial intelligence in ophthalmology and telematics are helping to solve the imbalance in ophthalmology development. From the perspective of the application of ophthalmic artificial intelligence in telemedicine, this paper mainly analyzes and summarizes the development degree, advantages and existing problems of artificial intelligence in the telemedicine of ophthalmic diseases in China, and discusses the prospect of the application of ophthalmic artificial intelligence in telemedicine.
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.
目的：分析眼科护理对人工智能技术应用的内在需求，为眼科医院临床的人工智能技术开发及应用提供导向与依据。方法：采用整群抽样和单纯随机抽样相结合的方法，于2019年7月至2019年8月，对抽取的中山大学中山眼科中心，中山大学附属第一医院、珠海市人民医院、无锡人民医院、新疆维吾尔族自治区人民医院等目标医院其中的眼科护理人员进行问卷调查，内容包括一般资料及人工智能需求等。结果：调查对象绝大部分来自三级甲等医院(89.2%)，以华南地区为主(87.2%)，人工智能在眼科临床护理应用的需求多种多样，其中以健康教育、接诊与分诊、患者回访领域需求最强烈，分别占比95.7%、93.5%、93.2%。结论：人工智能在眼科临床护理应用有较强及多样化的需求，应结合实际需求为导向，重点推进人工智能在眼科患者健康教育等相关应用的研发。
Objective: To analyze the internal demands of the application of artificial intelligence technology to ophthalmic care, and provide guidance and basis for the development and application of artificial intelligence technology in ophthalmic hospitals. Methods: Using the method of combining cluster sampling with simple random sampling, a questionnaire survey was conducted on the ophthalmic nursing staff in the selected target hospitals from July to August 2019, which included general information and artificial intelligence needs. Results: Most of the respondents came from the third-class hospitals (89.2%), and hospitals in South China account for 87.2% of them. There are diverse demands of artificial intelligence in ophthalmology clinical nursing applications, including health education, clinical reception and triage, patients return visits, which have the strongest demand for the artificial intelligence, accounting for 95.7%, 93.5%, and 93.2%, respectively. Conclusion: The application of artificial intelligence in ophthalmic clinical nursing has strong and diversified demands, and the research and development of artificial intelligence in the health education of ophthalmic patients and other related applications should be promoted according to the actual demands.
近视是危害儿童青少年视力最常见的眼部疾病，高度近视对视功能造成极大的威胁。近年来，我国近视发病率逐年升高，对近视筛查与防控的需求也不断增加，随着人工智能理论与技术的不断发展与成熟，可以辅助眼科医生进行近视筛查、诊断与治疗。本文将简要介绍人工智能在近视的筛查、预测、检测、病理性近视以及角膜屈光手术中的应用，浅谈了目前人工智能在研究中存在的可比度较低、影像要求较高、可解释性较低及隐私保护等问题，并展望人工智能在近视相关领域的应用前景。
Myopia is the most common ocular disease that harms the vision of children and adolescents. High myopia poses a great threat to visual function. The incidence of myopia in China has been increasing in recent years, and the demand for myopia screening, prevention and control has also expanded. With the continuous development of artificial intelligence theory and technology, Artificial intelligence can assist ophthalmologists in myopia screening, diagnosis and treatment. This review will briefly introduce artificial intelligence in the screening, prediction, and detection of myopia; also, the application in pathological myopia and corneal refractive surgery. This review will discuss some problems of current artificial intelligence research, such as low comparability, high image requirements, low interpretability, privacy protection, and the application prospects of artificial intelligence in myopia.
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.
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.
