This paper reports on the application of an artificial neural network to the clinical analysis of ophthalmological data. In particular a 2-dimensional Kohonen self-organising feature map (SOM) is used to analyse visual field data from glaucoma patients. Importantly, the paper addresses the problem of how the SOM can be utilised to accommodate the noise within the data. This is a particularly important problem within longitudinal assessment, where detecting significant change is the crux of the problem in clinical diagnosis. Data from 737 glaucomatous visual field records (Humphrey Visual Field Analyzer, program 24-2) are used to train a SOM with 25 nodes organised on a square grid. The SOM clusters the data organising the output map such that fields with early and advanced loss are at extreme positions, with a continuum of change in place and extent of loss represented by the intervening nodes. For each SOM node 100 variants, generated by a computer simulation modelling the variability that might be expected in a glaucomatous eye, are also classified by the network to establish the extent of noise upon classification. Field change is then measured with respect to classification of a subsequent field, outside the area defined by the original field and its variants. The significant contribution of this paper is that the spatial analysis of the field data, which is provided by the SOM, has been augmented with noise analysis enhancing the visual representation of longitudinal data and enabling quantification of significant class change.

译文

本文报告了人工神经网络在眼科数据临床分析中的应用。特别是二维Kohonen自组织特征图 (SOM) 用于分析青光眼患者的视野数据。重要的是,本文解决了如何利用SOM来容纳数据中的噪声的问题。在纵向评估中,这是一个特别重要的问题,在纵向评估中,检测出明显的变化是临床诊断问题的关键。来自737青光眼视野记录 (Humphrey视野分析仪,程序24-2) 的数据用于训练具有25个节点的SOM在方形网格上。SOM对组织输出图的数据进行聚类,以使具有早期和高级损失的字段处于极端位置,并且由中间节点表示连续的变化和损失程度。对于每个SOM节点100变体,由对青光眼眼中可能预期的可变性建模的计算机模拟生成,也由网络分类以建立分类时的噪声程度。然后,针对原始字段及其变体定义的区域之外的后续字段的分类来测量字段变化。本文的重要贡献在于,由SOM提供的现场数据的空间分析已通过噪声分析得到了增强,从而增强了纵向数据的视觉表示并能够量化重大的类别变化。

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