ArticleCureus2023
Automatic Classification of Antinuclear Antibody Patterns With Machine Learning.
Article in Cureus, 2023. 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, 7 citations in OpenAlex.
- Deep learning for automated classification of antinuclear antibody patterns on HEp-2 indirect immunofluorescence images.Immunologic research · 2026Article
- 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
- Article
- Artificial intelligence applications for immunology laboratory: image analysis and classification study of IIF photos.Immunologic research · 2024Article
- Comparison of the Capacity of Several Machine Learning Tools to Assist Immunofluorescence-Based Detection of Anti-Neutrophil Cytoplasmic Antibodies.International journal of molecular sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antinuclear antibodies (ANA) are important diagnostic markers in many autoimmune rheumatological diseases. The indirect immunofluorescence assay applied on human epithelial cells generates images that are used in the detection of ANA. The classification of these images for different ANA patterns requires human experts. It is time-consuming and subjective as different experts may label the same image differently. Therefore, there is an interest in machine learning-based automatic classification of ANA patterns. In our study, to build an application for the automatic classification of ANA patterns, we construct a dataset and learn a deep neural network with a transfer learning approach. We show that even in the existence of a limited number of labeled data, high accuracies can be achieved on the unseen test samples. Our study shows that deep learning-based software can be built for this task to save expert time.
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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.