Novel opportunities in automated classification of antinuclear antibodies on HEp-2 cells

The recommended method for antinuclear antibodies (ANA) detection is IIF but it is influenced by many different factors. In order to pursue a high image quality without artefacts and to reduce inter-observer variability, this study aims to evaluate the reliability of using automatically acquired digital images for diagnostic purposes. In this paper we present SLIM-system a comprehensive system that supports the two sides of IIF tests classification. It is based on two systems: the first labels the fluorescence intensity, whereas the second recognizes the staining pattern of positive wells. We populated a dataset of 600 images obtained from sera screened for ANA by IIF on Hep-2 cells. The error rate has been evaluated according to eight-fold cross validation method; the rates reported in the following are the mean of the tests. Performance of the system in positive/negative recognition ranges from 87% up to more than 94%. Staining pattern classification accuracy of main classes ranges from 71% to 74%. The system provides high and reliable identification of negative samples and a flexibility that permits to use this application for different purposes. The analysis of its perspective performance shows the system potential in lowering the method variability, in increasing the level of standardization and in reducing the specialist workload of more than 80%. Our data represent a first step to validate the use of Computer Aided Diagnosis (CAD), thus offering an opportunity for standardizing and automatizing the detection of ANA by IIF.