ArticleDiagnostics (Basel, Switzerland)2021
Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A human epithelial cell cycle dataset.Scientific data · 2026Article
- Artificial intelligence for automated classification of antinuclear-antibody indirect immunofluorescence patterns.EULAR rheumatology open · 2026Article
- Review
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- 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
- Recognition of rare antinuclear antibody patterns based on a novel attention-based enhancement framework.Briefings in bioinformatics · 2024Article
- Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records.BioData mining · 2024Article
- Editorial: Artificial intelligence in rheumatology and musculoskeletal diseases.Frontiers in medicine · 2024Article
- Biomarkers in Systemic Sclerosis: An Overview.Current issues in molecular biology · 2023Review
- Article
- Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning.Computational intelligence and neuroscience · 2023Article
- Artificial Intelligence and Deep Learning for Rheumatologists.Arthritis & rheumatology (Hoboken, N.J.) · 2022Review
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAntinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading.
methods51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners.
resultsThe mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (κ) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers.
conclusionsThis study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.
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