• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of glaucoma >Trained artificial neural network for glaucoma diagnosis using visual field data: a comparison with conventional algorithms.
【24h】

Trained artificial neural network for glaucoma diagnosis using visual field data: a comparison with conventional algorithms.

机译：训练有素的人工神经网络使用视野数据诊断青光眼：与传统算法的比较。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

PURPOSE: To evaluate and confirm the performance of an artificial neural network (ANN) trained to recognize glaucomatous visual field defects, and compare its diagnostic accuracy with that of other algorithms proposed for the detection of visual field loss. METHODS: SITA Standard 30-2 visual fields, from 100 glaucoma patients and 116 healthy participants, formed the data set. Our ANN was a previously described fully trained network using scored pattern deviation probability maps as input data. Its diagnostic accuracy was compared to that of the Glaucoma Hemifield Test, the Pattern Standard Deviation index at the P<5% and <1%, and also to a technique based on the recognizing clusters of significantly depressed test points. RESULTS: The included tests had early to moderate visual field loss (median MD=-6.16 dB). ANN achieved a sensitivity of 93% at a specificity level of 94% with an area under the receiver operating characteristic curve of 0.984. Glaucoma Hemifield Test attained a sensitivity of 92% at 91% specificity. Pattern Standard Deviation, with a cut off level at P<5% had a sensitivity of 89% with a specificity of 93%, whereas at P<1% the sensitivity and specificity was 72% and 97%, respectively. The cluster algorithm yielded a sensitivity of 95% and a specificity of 82%. CONCLUSIONS: The high diagnostic performance of our ANN based on refined input visual field data was confirmed in this independent sample. Its diagnostic accuracy was slightly to considerably better than that of the compared algorithms. The results indicate the large potential for ANN as an important clinical glaucoma diagnostic tool.
机译：目的：评估和确认经过训练可识别青光眼视野缺损的人工神经网络（ANN）的性能，并将其诊断准确性与为检测视野缺损而提出的其他算法进行比较。方法：来自100名青光眼患者和116名健康参与者的SITA标准30-2视野形成了数据集。我们的人工神经网络是先前描述的经过充分训练的网络，使用得分模式偏差概率图作为输入数据。将其诊断准确度与青光眼Hemifield检验的诊断准确性，P <5％和<1％的模式标准偏差指数进行了比较，还与基于识别显着降低的测试点簇的技术进行了比较。结果：纳入的测试有早期到中度的视野损失（中值MD = -6.16 dB）。人工神经网络在94％的特异性水平下获得了93％的灵敏度，接收器工作特性曲线下方的面积为0.984。青光眼半场试验以91％的特异性获得了92％的灵敏度。模式标准偏差的截止水平为P <5％，灵敏度为89％，特异性为93％，而在P <1％时，灵敏度和特异性分别为72％和97％。聚类算法产生的灵敏度为95％，特异性为82％。结论：在独立样本中证实了基于改进的输入视野数据的人工神经网络的高诊断性能。它的诊断准确度比比较算法略有提高。结果表明，人工神经网络具有作为重要的临床青光眼诊断工具的巨大潜力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号