Deep learning-based automated grading of visual impairment in cataract patients using fundus images

Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.

医院核酸检测看板软件设计与应用

目的:通过利用信息化手段动态展示院内核酸检测情况的实时数据,为医院管理部门决策提供数据支撑。方法:基于企业微信平台,采用B/S架构,运用数据中心进行数据归集与处理。结果:打通了人事管理系统与实验室信息管理系统之间的信息壁垒,把系统中的全量数据提供给基础数据库进行关联分析整合,全面解决了医院职能部门对于新冠病毒感染疫情防控中人员核酸检测的信息化需求问题。结论:通过看板软件做到了数据实时监管,改变了原有人工纸质统计的工作方式,有效提升了工作效率,并满足了疫情防控工作中对数据的及时性、准确性和一致性的要求。

医疗影像云的构建实践与探讨

目的通过医疗影像云的建设,实现影像数据的云端备份和归档,以及快速调阅和扩展应用。方法运用云计算、云存储等技术将医院内影像数据上传至医疗影像云,并使用对象存储作为影像数据的存储介质。结果实现了影像数据的全终端调阅查询,提升了医疗协作的效率和质量。结论医疗影像云不仅能减轻医院本地机房的运维成本和压力,在保证影像数据存储和传输安全的前提下,还能使医生高效、便捷地对云上影像数据加以利用,对医院和社会具有较大价值。

新冠肺炎疫情下互联网慢病管理探讨

慢性病患者在新冠肺炎疫情中属于高危易感人群,在新冠肺炎疫情防控的新形势下,慢病管理模式也面临新的转变。通过“互联网+医疗”的技术手段,开展慢病互联网线上预约与诊疗,开展线下药品物流配送,建立贯穿健康全过程的健康档案,构建全方位健康数据监测和一体化慢病管理模式,可实现智能化健康监护计划管理。慢病管理模式的互联网化提升了医疗服务的可及性,提高了健康数据的完整性,提高了医患沟通的效率和效果。

三种多焦点人工晶状体植入术后的临床观察及视觉质量的对比研究

目的比较三种多焦点人工晶状体植入术后的视觉质量。方法将56例(72眼)白内障患者分为3组,每组24眼,分别植入ZEISS AT LISA tri 839MP人工晶状体、Oculentis L-313 MF30人工晶状体和Alcon SN6AD1人工晶状体。术后1d、1周、1个月及3个月观察3组远(5 m)、中(80 cm)、近(40 cm)视力及并发症等指标,术后3个月行对比敏感度测试,并绘制3组人工晶体离焦曲线,同时问卷调查3组的视觉满意度及脱镜率。结果3组多焦人工晶状体在提升患者远视力方面效果均突出,结果接近于正视力;3组术后裸眼中、近视力及矫正中、近视力差异均有统计学意义(均P<0.05)。3组对比敏感度检测结果均处于正常人水平。3组仅少数患者存在轻度视觉不良症状,脱镜率分别为95.83%、91.67%及83.33%。AT LISA tri 839MP人工晶体满意度高于其他两种(P<0.05)。结论新一代的三焦点人工晶状体ATLISAtri 839MP可有效提高白内障患者术后全程视力,在视力提升、脱镜率及患者满意度方面优于L-313 MF30及SN6AD1人工晶状体。