论文部分内容阅读
背景:既往的研究揭示,与正常人相比,弱视患者存在多种视觉缺陷。通过工程化的视知觉系统对人的视觉系统进行相关检测就能够发现弱视患者。目的:通过检测弱视患者的多项视知觉功能,从中筛选出敏感性强的指标用于弱视筛查,并由此建立起一种基于互联网的弱视筛查方法。设计、时间及地点:随机、盲法、对照试验,于 2008-09/11 在广西壮族自治区人民医院视光科完成。对象:从广西壮族自治区人民医院视光科接受视知觉功能检测的志愿者中随机抽取 80人,年龄 4~19 岁,在排除眼部器质性疾病的前提下,按照矫正视力是否达 4.9 以上将其分成正常组和弱视组,其中正常组 40 人,弱视组 40 人。方法:运用视知觉检测系统对每位受试者的多项视知觉功能指标进行检测,将采集到的单眼和双眼数据进行 Fishher 判别分析,从中筛选出敏感性指标并建立判别方程用于弱视筛查。主要观察指标:受试者的年龄、视觉噪声、位置辨别和轮廓整合等功能。结果:判别结果显示,年龄、视觉噪声、位置辨别和轮廓整合等指标意义较大,交互验证法显示判别正确率达 92.5%,具备临床应用价值。弱视的判别函数式为 :Y1=1.175X1+0.786X2+0.928X3+1.061X4- 0.225X5+2.547X6+1.313X7-18.651;正常的判别函数式为:Y2=1.369 X1+1.728 X2+ 1.779 X3+1.549 X4-1.912 X5+2.665 X6+0.387 X7 -26.640。结论:视知觉检测系统中的视觉噪声、位置辨别和轮廓整合程序可用于儿童弱视筛查,并可借助互联网发挥更加积极而深远的作用。
Background: Previous studies have revealed a variety of visual deficits in amblyopic subjects compared to normal subjects. Amblyopia patients can be detected by detecting the human visual system through an engineering visual perception system. OBJECTIVE: To screen amblyopia screening by detecting a number of visual acuity function in amblyopia patients, and to establish a screening method for amblyopia based on internet. DESIGN, TIME AND SETTING: A randomized, blinded, controlled trial was performed at the Department of Optiatrics, People’s Hospital of Guangxi Zhuang Autonomous Region, 2008-09 / 11. PARTICIPANTS: Eighty randomly selected volunteers from the Department of Optometry, People’s Hospital of Guangxi Zhuang Autonomous Region under the Visual acuity test, aged 4 to 19 years old, under the premise of excluding eye organic diseases, according to whether the corrected visual acuity reached 4.9 or above Will be divided into normal group and amblyopia group, of which 40 normal group, amblyopia group 40 people. Methods: Visual acuity testing system was used to detect multiple visual acuity functional indicators of each subject. Fishher discriminant analysis was performed on the monocular and binocular data collected. Sensitivity indices were selected and discrimination equations were established for amblyopia screening check. MAIN OUTCOME MEASURES: Subject age, visual noise, location discrimination, and contour integration. Results: The discriminant results showed that the indicators such as age, visual noise, location discrimination and contour integration were significant, and the interactive verification showed that the correct rate was 92.5%, which had clinical value. The discriminant function for amblyopia is: Y1 = 1.175X1 + 0.786X2 + 0.928X3 + 1.061X4- 0.225X5 + 2.547X6 + 1.313X7-18.651; normal discriminant function is: Y2 = 1.369 X1 + 1.728 X2 + 1.779 X3 + 1.549 X4-1.912 X5 + 2.665 X6 + 0.387 X7 -26.640. CONCLUSIONS: The visual noise, position discrimination, and contour integration programs in visual perception detection systems can be used to screen children for amblyopia and can play a more positive and far-reaching role with the help of the Internet.