Background and objective Morphological identification of peripheral leukocytes is a complex and time-consuming task, having especially high requirements for personnel expertise. This study is to investigate the role of artificial intelligence (AI) in assisting the manual leukocyte differentiation of peripheral blood. Methods A total of 102 blood samples that triggered the review rules of hematology analyzers were enrolled. The peripheral blood smears were prepared and analyzed by Mindray MC-100i digital morphology analyzers. Two hundreds leukocytes were located and their cell images were collected. Two senior technologists labeled all cells to form standard answers. Afterward, the digital morphology analyzer unitized AI to pre-classify all cells. Ten junior and intermediate technologists were selected to review the cells with the AI pre-classification, yielding the AI-assisted classifications. Then the cell images were shuffled and re-classified without AI. The accuracy, sensitivity and specificity of the leukocyte differentiation with or without AI assistance were analyzed and compared. The time required for classification by each person was recorded. Results For junior technologists, the accuracy of normal and abnormal leukocyte differentiation increased by 4.79% and 15.16% with the assistance of AI. And for intermediate technologists, the accuracy increased by 7.40% and 14.54% for normal and abnormal leukocyte differentiation, respectively. The sensitivity and specificity also significantly increased with the help of AI. In addition, the average time for each individual to classify each blood smear was shortened by 215 s with AI. Conclusion AI can assist laboratory technologists in the morphological differentiation of leukocytes. In particular, it can improve the sensitivity of abnormal leukocyte differentiation and lower the risk of missing detection of abnormal WBCs.

No abstract

Objective: To investigate the serum level of soluble CD27 (sCD27) and its potential clinical significance in rheumatoid arthritis (RA). Methods: The serum sCD27 levels in both RA and health controls (HCs) were detected by enzyme-linked immunosorbent assay. The medical information and laboratory data of the patients were collected. The serum sCD27 levels in RA with different clinical features were analyzed, and the correlation between the clinical data and serum sCD27 levels were also analyzed. Independent samples t test, Mann-Whitney U-test or Wilcoxon signed-rank test, Spearman correlation were used for statistical analysis. Results: The levels of sCD27 were elevated in the RA patients (5647 ± 6526 pg/mL) than those of HCs (1659 ± 648 pg/mL) (P < 0.001). In addition, serum sCD27 levels were correlated with age, erythrocyte sedimentation rate, C-reactive protein (CRP), rheumatoid factor (RF), immunoglobulin A, immunoglobulin G, Complement 4 and disease activity score in 28 joints in RA patients. The levels of sCD27 were higher in the RF-positive RA patients (6522 ± 7447 pg/mL) than the RF-negative patients (3902 ± 2098 pg/mL), and the similar finding was also observed in anti-cyclic citrullinated peptide (anti-CCP) antibody positive (6241 ± 7207 pg/mL) and anti-CCP negative RA patients (4183 ± 2187 pg/mL). The RA patients with increased sCD27 and increased CRP showed the highest ratio of osteoporosis complication (P < 0.001). Conclusion: Serum sCD27 might be a promising biomarker which could reflect both disease activity and humoral immunity activity in RA.

No abstract