青光眼性视野缺损的空间分类
Spatial classification of glaucomatous visual field loss.
作者信息
Henson D B, Spenceley S E, Bull D R
机构信息
Department of Ophthalmology, University of Manchester.
出版信息
Br J Ophthalmol. 1996 Jun;80(6):526-31. doi: 10.1136/bjo.80.6.526.
AIMS
To develop and describe an objective classification system for the spatial patterns of visual field loss found in glaucoma.
METHODS
The 560 Humphrey visual field analyser (program 24-2) records were used to train an artificial neural network (ANN). The type of network used, a Kohonen self organising feature map (SOM), was configured to organise the visual field defects into 25 classes of superior visual field loss and 25 classes of inferior visual field loss. Each group of 25 classes was arranged in a 5 by 5 map.
RESULTS
The SOM successfully classified the defects on the basis of the patterns of loss. The maps show a continuum of change as one moves across them with early loss at one corner and advanced loss at the opposite corner.
CONCLUSIONS
ANNs can classify visual field data on the basis of the pattern of loss. Once trained the ANN can be used to classify longitudinal visual field data which may prove valuable in monitoring visual field loss.
目的
开发并描述一种针对青光眼视野缺损空间模式的客观分类系统。
方法
使用560型Humphrey视野分析仪（程序24-2）记录来训练人工神经网络（ANN）。所使用的网络类型，即Kohonen自组织特征映射（SOM），被配置为将视野缺损组织成25类上半视野缺损和25类下半视野缺损。每组25类被排列成一个5×5的映射图。
结果
SOM根据缺损模式成功地对缺损进行了分类。这些映射图显示了一种连续变化，当从一个角向另一个角移动时，一个角是早期缺损，另一个角是晚期缺损。
结论
人工神经网络可以根据缺损模式对视野数据进行分类。一旦经过训练，人工神经网络可用于对纵向视野数据进行分类，这在监测视野缺损方面可能具有重要价值。
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