Identifying the measurement noise in glaucomatous testing: an artificial neural network approach

Diagnosis of visual function losses in glaucomatous patients depends to a large extent on the analysis of the data collected from corresponding psychophysical tests. One of the main difficulties in obtaining reliable data from patients in these tests is the measurement noise caused by the learning effect, inattention, failure of fixation, fatigue etc. Using Kohonen's self-organising feature map, we have developed a computational method to distinguish between the noise and true measurement and to provide an instant assessment of reliability of the computer-based visual function test. In particular we have experimented with 270 test records from glaucoma patients and glaucoma suspects and found that this method provides a satisfactory way of locating and rejecting noise in the test data, an improvement over conventional statistical methods. This method can also provide doctors with a clear view of the patient's behaviour during the test, thus assisting in their diagnostic decision making process.