ArticleArthritis research & therapy2014
The classification of Crithidia luciliae immunofluorescence test (CLIFT) using a novel automated system.
Article in Arthritis research & therapy, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Advances and pitfalls in anti-double-stranded DNA autoantibody detection and interpretation.World journal of biological chemistry · 2026Review
- Comparative evaluation of chemiluminescent immunoassay and radioimmunoassay for the detection of anti-double-stranded DNA antibodies.Journal of rheumatic diseases · 2026Article
- Article
- Automation, consolidation, and integration in autoimmune diagnostics.Auto- immunity highlights · 2015Article
- Automated Evaluation of Crithidia luciliae Based Indirect Immunofluorescence Tests: A Novel Application of the EUROPattern-Suite Technology.Journal of immunology research · 2015Article
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Authors and funding
8 authors.
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Abstract
introductionIn recent years, there has been an increased demand for computer-aided diagnosis (CAD) tools to support clinicians in the field of indirect immunofluorescence. To this aim, academic and industrial research is focusing on detecting antinuclear, anti-neutrophil, and anti-double-stranded (anti-dsDNA) antibodies. Within this framework, we present a CAD system for automatic analysis of dsDNA antibody images using a multi-step classification approach. The final classification of a well is based on the classification of all its images, and each image is classified on the basis of the labeling of its cells.
methodsWe populated a database of 342 images--74 positive (21.6%) and 268 negative (78.4%)-- belonging to 63 consecutive sera: 15 positive (23.8%) and 48 negative (76.2%). We assessed system performance by using k-fold cross-validation. Furthermore, we successfully validated the recognition system on 83 consecutive sera, collected by using different equipment in a referral center, counting 279 images: 92 positive (33.0%) and 187 negative (67.0%).
resultsWith respect to well classification, the system correctly classified 98.4% of wells (62 out of 63). Integrating information from multiple images of the same wells recovers the possible misclassifications that occurred at the previous steps (cell and image classification). This system, validated in a clinical routine fashion, provides recognition accuracy equal to 100%.
conclusionThe data obtained show that automation is a viable alternative for Crithidia luciliae immunofluorescence test analysis.
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