Original articlesNeural networks to identify glaucomatous visual field progression
Section snippets
Design
This study was done as an observational retrospective cohort study. The study took place at The Study Center: Jules Stein Eye Institute, UCLA School of Medicine (Los Angeles, California, USA).
Patients
Data from two AGIS testing centers (Yale University and Georgetown University), collected from October 1988 to January 2000, were used to develop and test the neural network. Study eyes were required to have uncontrolled intraocular pressure (IOP). This means that despite maximally tolerated and effective available treatment, the last two IOP measurements, made on separate days, were both above a critical level, as determined by the AGIS protocol. For an eye with little or no visual field
Results
Data from 80 patients enrolled in the AGIS were used in the current study. The mean age at the time of the initial reference examination was 67.4 ± 7.3 years. The mean follow-up period was 7.2 ± 2.3 years, and the mean duration between adjacent examinations was 0.46 ± 0.39 years (Table 1). Twenty-six patients had both eyes enrolled in AGIS. Analyses for data from these patients were performed in two ways: (1) with one eye randomly selected and (2) with both eyes included.
The following results
Discussion
Physician decision making is often surrounded by a degree of uncertainty, which arises in part from noise in diagnostic tests. A physician must be able to process several pieces of information at once and attempt to distinguish the signal (true diagnosis) from the noise (random fluctuation). This uncertainty is especially large when following a glaucoma patient. In the visual field aspect of glaucoma, there are at least two reasons for the uncertainty: the lack of objectivity in evaluating
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