• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Graefe's archive for clinical and experimental ophthalmology: Albrecht von Graefes Archiv fur klinische und experimentelle Opthalmologie >Glaucoma detection and evaluation through pattern recognition in standard automated perimetry data.
【24h】

Glaucoma detection and evaluation through pattern recognition in standard automated perimetry data.

机译：青光眼通过标准自动视野检查数据中的模式识别进行检测和评估。

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

BACKGROUND: Perimetry remains one of the main diagnostic tools in glaucoma, and it is usually used in conjunction with evaluation of the optic nerve. This study assesses the capability of automatic pattern recognition methods, and in particular the support vector machines (SVM), to provide a valid clinical diagnosis classification of glaucoma based solely upon perimetry data. METHODS: Over 2,200 patient records were reviewed to produce an annotated database of 2,017 eyes. Visual field (VF) data were obtained with HFA II perimeter using the 24-2 algorithm. Ancillary information included treated and untreated intraocular pressure, cup-to-disk ratio, age, sex, central corneal thickness and family history. Ophthalmic diagnosis and classification of visual fields were provided by a consensus of at least two glaucoma experts. The database includes normal eyes, cases of suspect glaucoma, pre-perimetric glaucoma, and glaucoma with different levels of severity, as well as 189 eyes with neurologic or neuro-ophthalmologic defects. Support vector machines were trained to provide multi-level classifications into visual field and glaucoma diagnosis classes. RESULTS: Numerical validation indicates 70-90% expected agreement between multi-stage classifications provided by the automated system, using a hierarchy of SVM models, and glaucoma experts. Approximately 75% accuracy for the classification of glaucoma suspect and pre-perimetric glaucoma (which by definition do not exhibit glaucomatous defects) indicates the ability of the numerical model to discern subtle changes in the VF associated with early stages of glaucoma. The Glaucoma Likelihood Index provides a single number summary of classification results. CONCLUSIONS: Automatic classification of perimetry data may be useful for glaucoma screening, staging and follow-up.
机译：背景：视野测量仍然是青光眼的主要诊断工具之一，通常与视神经评估结合使用。这项研究评估了自动模式识别方法（特别是支持向量机（SVM））仅基于视野检查数据即可提供有效的青光眼临床诊断分类的能力。方法：回顾了超过2,200例患者记录，以产生一个包含2,017只眼睛的注释数据库。使用24-2算法使用HFA II周边获得视野（VF）数据。辅助信息包括治疗和未治疗的眼内压，杯盘比，年龄，性别，中央角膜厚度和家族史。至少两名青光眼专家的共识提供了眼科诊断和视野分类。该数据库包括正常眼睛，可疑青光眼，围前期青光眼和严重程度不同的青光眼病例，以及189眼有神经或眼科缺陷的眼。支持向量机经过培训可以提供视野和青光眼诊断类别的多级分类。结果：数值验证表明，使用SVM模型的层次结构和青光眼专家，由自动化系统提供的多阶段分类之间的预期一致性为70-90％。对可疑青光眼和围前期青光眼进行分类的准确率大约为75％（根据定义，它们不显示青光眼缺陷），表明该数值模型能够识别与青光眼早期相关的VF的细微变化。青光眼可能性指数提供了分类结果的单个数字摘要。结论：自动分类视野检查数据可能有助于青光眼的筛查，分期和随访。

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