远程医疗筛查糖尿病视网膜病变的卫生经济学评价

糖尿病视网膜病变(diabetic retinopathy, DR)是糖尿病常见的微血管并发症, 是成年人低视力和致盲的主要原因。筛查可以使DR得到早期诊断和治疗, 从而减少患者视力损害, 提高生活质量。在组织大规模筛查前, 国家医疗卫生部门通常会评估筛查的效果和筛查产生的社会成本消耗。近年来, 许多国家和地区在基层卫生机构应用远程医疗开展DR筛查项目, 并使用卫生经济学方法评价筛查的成本效益。通过对单个或者多个敏感性因素进行分析来制定筛查的个性化方案使成本效益最大化。近年国内外关于远程医疗在DR筛查领域的研究显示, 大多数远程医疗在卫生经济学评价上具有成本效益, 制定个性化筛查方案可将成本效益最优化。(国际眼科纵览, 2023, 47:36-42)     

人工智能在宁夏银川社区糖尿病视网膜病变远程筛查中的应用

目的:评估人工智能(AI)辅助诊断系统在宁夏银川社区糖尿病视网膜病变(DR)筛查中的应用效果。方法:收集2020-07/2021-07就诊于宁夏银川两个社区卫生服务中心的2型糖尿病患者1 358例2 707眼的眼底彩照,采用Eye Wisdom AI眼病辅助筛查和诊断系统自动检测分析出血、微动脉瘤以及视网膜内微血管异常等DR的特征性改变。根据其国际分期的标准对眼底彩照检测结果进行自动分级,由人工分析组进行图像判读后反馈结果,分析AI辅助筛查系统诊断DR的灵敏度、特异度、误诊率及漏诊率,比较AI与人工分析的一致性,对AI筛查系统与人工分析的结果做Kappa一致性检验。结果:与人工分析相比,AI诊断有无DR的灵敏度为91.84%,特异度为99.06%,漏诊率为8.16%,误诊率为0.94%,对于二者诊断结果的一致性分析Kappa值为0.817(P<0.001)。与人工分析相比,AI组检测无DR的灵敏度为99.06%,特异度为91.84%;检测轻度NPDR的灵敏度为85.36%,特异度为98.52%;中度NPDR的灵敏度为81.53%,特异度为98.55%;重度NPDR的灵敏度为70%...     

人工智能在眼底影像分析中的研究进展及应用现状

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。     

人工智能在视网膜疾病中的应用

视网膜疾病是眼科学领域最具研究挑战的病种之一,其发病机制复杂,对视功能损伤很大,是人类视力严重丧失的主要原因。近年来人工智能(artificial intelligence,AI)的发展和应用为视网膜疾病的解析提供了强有力工具。人工智能对常见视网膜疾病的应用主要包括早期筛查、诊断分级、疗效判定、治疗建议及预后发展等。但是任何技术的临床应用都有其局限性。本文将对AI在糖尿病视网膜病变(DR)、年龄相关性黄斑变性(ARMD)、早产儿视网膜病变(ROP)、青光眼性眼底改变(GON)等视网膜疾病中的应用和局限性进行综述。     

基于远程诊断系统的糖尿病眼病综合防治服务模式实践探讨

目的：总结浦东新区糖尿病眼病综合防治服务模式的实施成效,为建立完善以社区为基础的糖尿病眼病综合防治服务模式提供实践参考。

方法：于2018-08/2019-08将浦东新区45家社区纳入慢性病管理的糖尿病患者作为筛查对象,根据年龄、性别进行分层抽样,对纳入对象的远程筛查眼健康数据进行分析,并通过问卷调查掌握满意度及参与意愿。

结果：本次筛查纳入DM患者39 055例,均首次建立眼健康档案,远程诊断初步确诊为糖尿病视网膜病变(DR)者15.870%(6198/39055),其中轻度非增殖性DR、中度非增殖性DR、重度非增殖性DR及增殖性DR者分别为3.974%、9.904%、1.746%、0.246%; 需转诊率44.325%(17311/39055),实际复诊率5.846%(1012/17311)。此外,DM患者远程筛查满意度与参与意愿均在85%以上。

结论：基于远程诊断系统的糖尿病眼病综合防治服务模式对于实现糖尿病眼病早发现、早诊断、早干预是可行的,但在实际应用中还需进一步优化与完善。     

人工智能在青光眼诊疗中的应用

青光眼是世界首位不可逆致盲眼病,由于其隐匿性与渐进性,临床上早期诊断及监测青光眼进展是一项十分重要的工作。人工智能(AI)在医学领域发展迅速,AI及其子集机器学习(ML)、深度学习(DL)在青光眼中的研究与应用日臻成熟,促进了人们对青光眼的理解,极大地提高了青光眼筛查与诊断的准确性与效率,大大降低了检查成本。利用AI技术对青光眼患者进行早期筛查与诊断能降低患者视功能损伤的风险,其次可以对青光眼进展进行预测,设计个性化治疗方案,从而达到改善患者预后的目的。本文总结了AI在青光眼筛查、诊断、预后中的最新进展,以及在临床上的困难与挑战,并展望了AI在青光眼中的优势与未来发展趋势。     

重视糖尿病人群视网膜病变的全程监测

糖尿病性视网膜病变(DR)是最为常见的糖尿病眼部并发症, 是导致我国30岁以上成人视功能损伤的重要原因。定期进行眼底检查并合理控制血糖水平可预防98%致盲性DR。然而, 由于医疗资源分布不均, 患者健康意识薄弱, 目前仅50%～60%糖尿病患者每年接受DR筛查, 因此需要建立DR早筛、早防、早治疗和终身随访监测的防治体系。本文从DR全程监测的重要性、分级诊疗是DR的防控重点、重视青少年DR的随访监测3个方面展开论述, 强调应对不同类型糖尿病患者进行多级别、多维度、多模式精准全程监测, 以期促进临床提高DR的防治效率, 积极减轻与疾病相关的家庭和社会负担。     

New progress on the application of swept-source optical coherence tomography angiography in the diagnosis and treatment of diabetic retinopathy北大核心

糖尿病视网膜病变(DR)是糖尿病患者最常见的微血管并发症,也是全球致盲的主要原因之一,其诊断和治疗预后的判断至关重要。扫频源光学相干断层扫描血管成像(SS-OCTA)是近年来发展出的一项影像学检查方法,应用SS-OCTA可以准确地观察不同层次的视网膜、脉络膜毛细血管层及黄斑和视盘的微循环情况,对DR的诊断和预后判断提出了新理念,本文旨在对SS-OCTA在DR临床应用方面的新进展进行综述。     

人工智能在圆锥角膜中的应用研究进展

圆锥角膜是一种高发于青少年的致盲性角膜疾病,早期诊疗可有效减少疾病晚期造成的视力损害并改善其预后。基于机器学习和深度学习的人工智能(AI)在圆锥角膜领域的研究主要包括圆锥角膜的早期筛查诊断和严重度分级、圆锥角膜进展预测及术后疗效预测等。文章总结近年常见的AI在圆锥角膜中的主要应用研究进展,并对其面临的挑战与未来前景进行展望。     

早期筛查老年人群糖尿病视网膜病变的卫生经济学分析

目的从社会角度和卫生系统角度探讨中国50岁以上人群行糖尿病视网膜病变(DR)筛查的经济学效益。方法检索中国知网、万方数据库、PubMed及Medline等数据库,检索日期为自建库之始至2020年12月,搜集中国50岁以上人群DR的患病率、转归率、筛查依从性、治疗依从性、筛查敏感性、筛查特异性、DR不同分期的效用值及死亡率等指标。按照国际分级系统将DR分为不威胁视力DR、威胁视力DR,糖尿病性黄斑水肿及DR致盲等4个阶段。筛查项目、转诊检查及治疗费用来源于邯郸眼病研究和2020年首都医科大学附属北京同仁医院住院系统。使用TreeAge Pro软件构建DR筛查的马尔可夫模型,以社区筛查、远程筛查及人工智能(AI)辅助筛查为研究策略,以无筛查为对照策略,以我国农村和城市50岁以上人群为队列人群,比较不同策略的成本效益,并比较1~5年筛查间隔的成本效益。2020年中国农村和城市人均生产总值(GDP)分别为49000元和84000元。经济学效益以增量成本-效用比(ICUR)和增量成本-效果比(ICER)表示。成本效益阈值按照世界卫生组织规定的ICER或ICUR<3倍人均GDP为具有成本效益,<1倍人均GDP为具有高成本效益,>3倍人均GDP则不具备经济性。通过敏感性分析评价模型的稳健性。结果农村和城市人群的成本效益阈值分别为147000元和252000元。从社会角度看,对农村50岁以上人群进行社区、远程及AI辅助筛查的ICUR分别为4214元、2772元及3206元;每避免1个致盲年导致的ICER分别为216524元、152726元及164745元;对城市人群筛查的ICUR分别为7133元、5061元及5894元;每避免1个致盲年导致的ICER分别为57792元、37632元及55839元。与无筛查人群相比,农村和城市的ICUR以及城市人群的ICER均<1倍人均GDP;3种筛查方式对农村50岁以上人群的ICER均略高于成本效益阈值。从卫生系统角度看,3种筛查方式对农村和城市老年人群的ICUR和ICER均低于成本效益阈值。社区或AI辅助筛查对城市老年人群间隔2年以上具有成本效益,远程筛查间隔1年以上已具有成本效益;而对农村人群各筛查间隔均不具有成本效益。经概率敏感性分析,当成本效益阈值为3倍人均GDP时,农村老年人群社区、远程及AI辅助筛查成本效益概率分别为87.34%、87.56%及87.54%;城市老年人群筛查的成本效益概率分别为89.19%、89.48%及89.61%。当成本效益阈值为1倍人均GDP时,3种筛查方式对农村老年人群筛查有成本效益的概率分别为86.03%、86.84%及86.55%;城市老年人群的成本效益概率分别为87.83%、88.55%及88.62%。说明构建马尔可夫模型的设计和结果稳健,对参数的浮动不敏感。结论对我国50岁以上老年人群进行DR筛查具有成本效益,因农村和城市的经济发展水平和疾病负担不同,应制定适宜当地的DR筛查模式。     

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.     

Using artificial intelligence for diabetic retinopathy screening: Policy implications

Artificial intelligence (AI) has evolved over the last few years; its use in DR screening has been demonstrated in multiple evidences across the globe. However, there are concerns right from the data acquisition, bias in data, difficulty in comparing between different algorithm, challenges in machine learning, its application in different group of population, and human barrier to AI adoption in health care. There are also legal and ethical concerns related to AI. The tension between risks and concerns on one hand versus potential and opportunity on the other have driven a need for authorities to implement policies for AI in DR screening to address these issues. The policy makers should support and facilitate research and development of AI in healthcare, but at the same time, it has to be ensured that the use of AI in healthcare aligns with recognized standards of safety, efficacy, and equity. It is essential to ensure that algorithms, datasets, and decisions are auditable and when applied to medical care (such as screening, diagnosis, or treatment) are clinically validated and explainable. Policy frameworks should require design of AI systems in health care that are informed by real-world workflow and human-centric design. Lastly, it should be ensured that healthcare AI solutions align with all relevant ethical obligations, from design to development to use and to be delivered properly in the real world.     

Review of retinal cameras for global coverage of diabetic retinopathy screening

The global burden of diabetes has resulted in an increase in the prevalence of diabetic retinopathy (DR), a microvascular complication of diabetes. Lifelong repetitive screening for DR is essential for early detection and timely management to prevent visual impairment due to the silent sight-threatening disorder. Colour fundus photography (CFP) is helpful for documentation of the retinopathy as well as for counselling the patient. CFP has established roles in DR screening, detection, progression and monitoring of treatment response. DR screening programmes use validated mydriatic or non-mydriatic fundus cameras for retinal imaging and trained image graders identify referable DR. Smartphone-based fundus cameras and handheld fundus cameras that are cost-effective, portable and easy to handle in remote places are gaining popularity in recent years. The images captured with these low-cost devices can be immediately sent to trained ophthalmologists for grading of DR. Recent increase in numbers of telemedicine programmes based on imaging with digital fundus cameras and remote interpretation has facilitated larger population coverage of DR screening and timely referral of those with sight-threatening DR to ophthalmologists. Good-quality retinal imaging and accurate diagnosis are essential to reduce inappropriate referrals. Advances in digital imaging such as ultra-wide field imaging and multi-modal imaging have opened new avenues for assessing DR. Fundus cameras with integrated artificial intelligence (AI)-based automated algorithms can also provide instant DR diagnosis and reduce the burden of healthcare systems. We review the different types of fundus cameras currently used in DR screening and management around the world.Subject terms: Retinal diseases, Outcomes research, Medical imaging     

人工智能在视光学领域中的研究进展

近年来,随着计算机科学技术的不断提高,以深度学习(DL)为基础的人工智能(AI)技术得到了飞速的发展,引起了全球的广泛关注。AI在医学领域的研究和应用已经取得了很大的进展,在眼科视光学领域,AI可对近视、斜视、弱视等疾病进行辅助诊断; 在圆锥角膜的筛查和早期诊断以及近视的预防和矫正中取得了良好的结果。尽管如此,AI在眼科的应用也存在巨大的限制和挑战,包括临床和技术挑战、算法结果的可解释性、医学法律问题等。本文综述了AI在眼科视光学领域诊疗中的应用、局限性及展望。     

人工智能糖尿病视网膜病变筛查系统与眼科医师诊断结果的一致性分析

目的　探讨人工智能糖尿病视网膜病变（diabetic retinopathy,DR）筛查系统与临床医师诊断的一致性。方法　收集2019年3月至2020年5月在十堰市太和医院眼科就诊的糖尿病患者325例（650眼）的眼科影像学检查资料,包括眼底彩色照相、黄斑区OCT、荧光素眼底血管造影（FFA）,由专家组和人工智能DR筛查系统分别给出诊断,专家组参考眼底彩色照相、黄斑区OCT及FFA检查结果做出诊断,人工智能DR筛查系统仅以眼底彩色照相结果作为诊断依据。以专家组诊断结果为标准,分析人工智能DR筛查系统诊断各级DR的敏感性、特异度、漏诊率和误诊率,对二者诊断结果的一致性行Kappa检验分析。结果　与专家组诊断结果相比,人工智能诊断有无DR的敏感性为94.78%,特异度为92.11%,二者诊断结果一致性分析的Kappa值为0.82（P<0.001)。与专家组诊断结果相比,人工智能诊断有转诊意义的DR的敏感性为93.75%,特异度为96.18%,二者对DR分期诊断的一致性分析的Kappa值为0.85（P<0.001)。结论　人工智能DR筛查系统与专家组诊断结果一致性高,具有较高的准确性和敏感度,可大规模用于DR的初级筛查工作。     

Automated detection of diabetic retinopathy in retinal images

Diabetic retinopathy (DR) is a disease with an increasing prevalence and the main cause of blindness among working-age population. The risk of severe vision loss can be significantly reduced by timely diagnosis and treatment. Systematic screening for DR has been identified as a cost-effective way to save health services resources. Automatic retinal image analysis is emerging as an important screening tool for early DR detection, which can reduce the workload associated to manual grading as well as save diagnosis costs and time. Many research efforts in the last years have been devoted to developing automatic tools to help in the detection and evaluation of DR lesions. However, there is a large variability in the databases and evaluation criteria used in the literature, which hampers a direct comparison of the different studies. This work is aimed at summarizing the results of the available algorithms for the detection and classification of DR pathology. A detailed literature search was conducted using PubMed. Selected relevant studies in the last 10 years were scrutinized and included in the review. Furthermore, we will try to give an overview of the available commercial software for automatic retinal image analysis.     

糖尿病视网膜病变人工智能自动诊断系统在社区和医院老年糖尿病患者中的应用效果分析

目的:比较基于彩色眼底像阅片的人工智能（AI）系统分别在社区和医院筛查和（或）诊断糖尿病视网膜病变（DR）的效率和差异,初步评价其应用价值。方法:回顾性和前瞻性相结合研究。回顾性收集2018年7月至2021年3月于河南省眼科研究所连续就诊的老年糖尿病患者1 608例的临床资料。其中,男性659例,女性949例;年龄中位...     

人工智能在青光眼诊断中的研究进展

近年来,随着以深度学习（DL）为代表的人工智能（AI）技术发展,为眼科领域带来了新的研究手段,提高了眼科疾病的筛查和诊断水平。目前,AI对糖尿病视网膜病变、白内障、早产儿视网膜病变、角膜炎等多种疾病的诊断效率较高。在青光眼方面,AI可用于分析眼底彩色照相、光学相干断层扫描（OCT）、视野等多模态影像综合评估结构及功能改变,从而提高青光眼的诊断水平。本文主要对AI在青光眼诊断中的研究进展进行综述,探讨其优势和现阶段的局限性。