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| 1 | Construction of databases:advances and significance in clinical research显示文摘Widely used in clinical research, the database is a new type of data management automation technology and the most efficient tool for data management. In this article, we first explain some basic concepts, such as the definition, classification, and establishment of databases. Afterward, the workflow for establishing databases, inputting data, verifying data, and managing databases is presented. Meanwhile, by discussing the application of databases in clinical research, we illuminate the important role of databases in clinical research practice. Lastly, we introduce the reanalysis of randomized controlled trials(RCTs) and cloud computing techniques, showing the most recent advancements of databases in clinical research. | Erping Long Bingjie Huang Liming Wang Xiaoyu Lin Haotian Lin | 2015 | Eye Science2015，30，4： | 1 |
| 2 | Natural selection contributes to the myopia epidemic显示文摘The prevalence of myopia, or nearsightedness, has skyrocketed in the past few decades, creating a public health crisis that is commonly attributed to lifestyle changes. Here we report an overall increase in the frequencies of myopia-associated mutant alleles over 25 years among participants of the UK Biobank.Although myopia itself appears to be selected against, many of the mutant alleles are associated with reproductive benefits, suggesting that reproduction-related selection inadvertently contributes to the myopia epidemic. We estimate that, in the UK alone, natural selection adds more than 100 000 myopia cases per generation, and argue that antagonistic pleiotropy be broadly considered in explaining the spreads of apparently disadvantageous phenotypes in humans and beyond. | Erping Long Jianzhi Zhang | 2021 | National Science Review2021，8，6： | 1 |
| 3 | Handwashing quality assessment via deep learning:a modelling study for monitoring compliance and standards in hospitals and communities显示文摘Background Hand hygiene can be a simple,inexpensive,and effective method for preventing the spread of infectious diseases.However,a reliable and consistent method for monitoring adherence to the guidelines within and outside healthcare settings is challenging.The aim of this study was to provide an approach for monitoring handwashing compliance and quality in hospitals and communities.Methods We proposed a deep learning algorithm comprising three-dimensional convolutional neural networks(3D CNNs)and used 230 standard handwashing videos recorded by healthcare professionals in the hospital or at home for training and internal validation.An assessment scheme with a probability smoothing method was also proposed to optimize the neural network’s output to identify the handwashing steps,measure the exact duration,and grade the standard level of recognized steps.Twenty-two videos by healthcare professionals in another hospital and 28 videos recorded by civilians in the community were used for external validation.Results Using a deep learning algorithm and an assessment scheme,combined with a probability smoothing method,each handwashing step was recognized(ACC ranged from 90.64%to 98.87%in the hospital and from 87.39%to 96.71%in the community).An assessment scheme measured each step’s exact duration,and the intraclass correlation coefficients were 0.98(95%CI:0.97-0.98)and 0.91(95%CI:0.88-0.93)for the total video duration in the hospital and community,respectively.Furthermore,the system assessed the quality of handwashing,similar to the expert panel(kappa=0.79 in the hospital;kappa=0.65 in the community).Conclusions This work developed an algorithm to directly assess handwashing compliance and quality from videos,which is promising for application in healthcare settings and communities to reduce pathogen transmis-sion. | Ting Wang Jun Xia Tianyi Wu Huanqi Ni Erping Long Ji-Peng Olivia Li Lanqin Zhao Ruoxi Chen Ruixin Wang Yanwu Xu Kai Huang Haotian Lin | 2022 | Intelligent Medicine2022，2，3： | 0 |
| 4 | 人工智能白内障协同管理的通用平台显示文摘目的: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。 | WU Xiaohang HUANG Yelin LIU Zhenzhen LAI Weiyi LONG Erping ZHANG Kai JIANG Jiewei LIN Duoru CHEN Kexin YU Tongyong WU Dongxuan LI Cong CHEN Yanyi ZOU Minjie CHEN Chuan ZHU Yi GUO Chong ZHANG Xiayin WANG Ruixin YANG Yahan XIANG Yifan CHEN Lijian LIU Congxin XIONG Jianhao GE Zongyuan WANG Dingding XU Guihua DU Shaolin XIAO Chi WU Jianghao ZHU Ke NIE Danyao XU Fan LV Jian CHEN Weirong LIU Yizhi 林浩添 无 王厚硕(审校) 罗明杰(审校) | 2023 | 眼科学报2023，38，10： | 0 |
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