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Updated: Feb 4, 2026

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate
Published on: January 17, 2018
Comparison of manual with artificial intelligence-aided interpretation of ANA HEp-2 IIF assay patterns in a clinical
Jonas Schmidt1, Sarina Weiß1, Frithjof Blessing2
1Institute for Laboratory Medicine, Singen, Germany.
Objectives:
Detection of antinuclear antibody (ANA) via indirect immunofluorescence (IIF) on HEp-2 cells is a screening test for the serological diagnosis of systemic autoimmune rheumatic diseases. Automated interpretation of ANA classification by novel artificial intelligence (AI)-aided pattern recognition was compared with expert reading under routine conditions.
Methods:
Consecutive serum samples of 2671 individuals referred to a routine laboratory were analysed for ANA titers and patterns using the automated interpretation system akironNeo. AI-based ANA detection was compared with independent classification by two experienced immunologists according to the international consensus on ANA patterns (ICAP) competence level.
Results:
Overall, a good agreement (κ > 0.60) between the different evaluators both for positive/negative classification of ANA fluorescence images as well as for the pattern classification of positive samples with a titer ≥ 1:320 was observed. Positive/negative differentiation at different cut-offs revealed κ values from 0.584 to 0.760 whereas corresponding pattern recognition for interphase, metaphase and cytoplasmic patterns demonstrated κ values from 0.560 to 0.736 for samples scored as positive by all three evaluators.
Conclusions:
The AI-based software showed a similar performance compared to human observers. AI-aided ANA image analysis can facilitate the diagnostic workflow of ANA IIF assays and reduce subjectivity during image classification.
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