ArticleInternational journal of rheumatology2016
The Reliability of a Novel Automated System for ANA Immunofluorescence Analysis in Daily Clinical Practice.
Article in International journal of rheumatology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Artificial intelligence for automated classification of antinuclear-antibody indirect immunofluorescence patterns.EULAR rheumatology open · 2026Article
- Pilot Study of AI-Assisted ANA Immunofluorescence Reading-Comparison with Classical Visual Interpretation.Journal of clinical medicine · 2025Article
- Artificial intelligence in rheumatology: status quo and quo vadis-results of a national survey among German rheumatologists.Therapeutic advances in musculoskeletal disease · 2024Article
- A Multicenter Analysis of Subjectivity of Indirect Immunofluorescence Test in Antinuclear Antibody Screening.Archives of rheumatology · 2019Article
- Performance analysis of automated evaluation of antinuclear antibody indirect immunofluorescent tests in a routine setting.Auto- immunity highlights · 2018Article
- Automated Processing and Evaluation of Anti-Nuclear Antibody Indirect Immunofluorescence Testing.Frontiers in immunology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated interpretation (AI) systems for antinuclear antibody (ANA) analysis have been introduced based on assessment of indirect immunofluorescence (IIF) patterns. The diagnostic performance of a novel automated IIF reading system was compared with visual interpretation (VI) of IIF in daily clinical practice to evaluate the reduction of workload. ANA-IIF tests of consecutive serum samples from patients with suspected connective tissue disease were carried out using HEp-2 cells according to routine clinical care. AI was performed using a visual analyser (Zenit G-Sight, Menarini, Germany). Agreement rates between ANA results by AI and VI were calculated. Of the 336 samples investigated, VI yielded 205 (61%) negative, 42 (13%) ambiguous, and 89 (26%) positive results, whereas 82 (24%) were determined to be negative, 176 (52%) ambiguous, and 78 (24%) positive by AI. AI displayed a diagnostic accuracy of 175/336 samples (52%) with a kappa coefficient of 0.34 compared to VI being the gold standard. Solely relying on AI, with VI only performed for all ambiguous samples by AI, would have missed 1 of 89 (1%) positive results by VI and misclassified 2 of 205 (1%) negative results by VI as positive. The use of AI in daily clinical practice resulted only in a moderate reduction of the VI workload (82 of 336 samples: 24%).
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.