Variation in antinuclear antibody detection: need for clear expectations and additional studies

Mapping theme evolution and identifying hotspots in biomarkers of systemic lupus erythematosus based on global research

Objective: To perform a bibliometric analysis in the field of biomarkers for systemic lupus erythematosus. Methods: Publications were from Web of Science. Microsoft Excel, VOSviewer, Science Mapping Analysis software Tool, CiteSpace and Tableau were used for analysis. Results: A total of 1112 publications were identified; 1503 institutions from 69 countries contributed, with the highest outputs from China and Karolinska University Hospital. Petri had a tremendous impact. Academic collaborations were localized. Lupus and Arthritis & Rheumatology were the top two journals in terms of publications and citations. Lymphocyte, autoantibody, type I interferon, genetic polymorphisms and urinary biomarkers have been high-frequency themes. Conclusion: Global collaboration needs to be further strengthened. Immune cell, cytokine and gene-level research as a whole and noninvasive tests are the future trends.

Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Background: Antinuclear 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. Methods: 51,694 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. Results: The 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. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.