Determining Progression in Glaucoma Using Visual Fields

Abstract

Abstract. The standardized visual field assessment, which measures visual function in 76 locations of the central visual area, is an important diagnostic tool in the treatment of the eye disease glaucoma. It helps determine whether the disease is stable or progressing towards blindness, with important implications for treatment. Automatic techniques to classify patients based on this assessment have had limited success, primarily due to the high variability of individual visual field measurements. The purpose of this paper is to describe the problem of visual field classification to the data mining community, and assess the success of data mining techniques on it. Preliminary results show that machine learning methods rival existing techniques for predicting whether glaucoma is progressing-though we have not yet been able to demonstrate improvements that are statistically significant. It is likely that further improvement is possible, and we encourage others to work on this important practical data mining problem.

An Application of Machine Learning Techniques for the Classification of Glaucomatous Progression

“…Several approaches have been used in the past to classify visual field data. One such approach is to consider each individual visual field measurement for each observation as a separate attribute, thus creating a total of 456 attributes (76x6) [9].…”

“…Because of the complexity of the data, many of the methods used for classification focus on the time series of a single attribute. [9] presents a comparison of several machine learning techniques such as linear support vector machines and point-wise linear-regression for data covering 8 sets of observations. [4] describes a study of 9 classification techniques but with data that covered only a single set of observations (no temporal data was available).…”

A framework for assessing glaucoma progression using structural and functional indices jointly

“…Identifying glaucoma progression is essential to determine whether treatment should be modified to preserve vision and also to define the endpoints in clinical trials [ 1 – 3 ]. The detection of progression is challenging however [ 3 ], in part because of measurement variability [ 4 – 6 ] and in part because structural and functional assessments do not always agree [ 4 , 7 – 9 ].…”