Abstract
In this paper we present an evaluation of the role of reliability indicators in glaucoma severity prediction. In particular, we investigate whether it is possible to extract useful information from tests that would be normally discarded because they are considered unreliable. We set up a predictive modelling framework to predict glaucoma severity from visual field (VF) tests sensitivities in different reliability scenarios. Three quality indicators were considered in this study: false positives rate, false negatives rate and fixation losses. Glaucoma severity was evaluated by considering a 3-levels version of the Advanced Glaucoma Intervention Study scoring metric. A bootstrapping and class balancing technique was designed to overcome problems related to small sample size and unbalanced classes. As a classification model we selected Naïve Bayes. We also evaluated Bayesian networks to understand the relationships between the different anatomical sectors on the VF map. The methods were tested on a data set of 28,778 VF tests collected at Moorfields Eye Hospital between 1986 and 2010. Applying Friedman test followed by the post hoc Tukey's honestly significant difference test, we observed that the classifiers trained on any kind of test, regardless of its reliability, showed comparable performance with respect to the classifier trained only considering totally reliable tests (p-value>0.01). Moreover, we showed that different quality indicators gave different effects on prediction results. Training classifiers using tests that exceeded the fixation losses threshold did not have a deteriorating impact on classification results (p-value>0.01). On the contrary, using only tests that fail to comply with the constraint on false negatives significantly decreased the accuracy of the results (p-value<0.01). Meaningful patterns related to glaucoma evolution were also extracted. Results showed that classification modelling is not negatively affected by the inclusion of less reliable tests in the training process. This means that less reliable tests do not subtract useful information from a model trained using only completely reliable data. Future work will be devoted to exploring new quantitative thresholds to ensure high quality testing and low re-test rates. This could assist doctors in tuning patient follow-up and therapeutic plans, possibly slowing down disease progression.
Highlights
Quality control during medical testing is an important issue in healthcare
We focus on the following questions: is the information contained in unreliable tests totally impractical for determining the future evolution of the disease? How do different reliability indicators impact on prediction results? Are these contributions significantly different?
As the Advanced Glaucoma Intervention Study (AGIS) score is defined for full threshold tests, we considered only these tests in the analysis
Summary
Quality control during medical testing is an important issue in healthcare. Introducing unreliable, low-quality and noisy information into the care process can lead to incorrect diagnoses and therapy plans. One of the most frequently applied techniques for ensuring high quality testing is to discard those tests that are considered unreliable. It is often difficult to define the criteria for stating which tests are really trustworthy. These criteria are usually defined by the manufacturers of the testing devices and are conservative and general in nature. We investigate whether it is possible to extract useful information from tests that may be discarded because they are considered unreliable. As a matter of fact, during clinical practice a qualitative evaluation of unreliable tests is often carried out to identify patterns that might
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