Background. Ultrasonography (US) is the most common method of identifying thyroid nodules, but US images require an experienced surgeon for identification. Many artificial intelligence (AI) techniques such as computer-aided diagnostic systems (CAD), deep learning (DL), and machine learning (ML) have been used to assist in the diagnosis of thyroid nodules, but whether AI techniques can improve the diagnostic accuracy of thyroid nodules still needs to be explored. Objective. To clarify the accuracy of AI-based thyroid nodule US images for differentiating benign and malignant thyroid nodules. Methods. A search strategy of “subject terms + key words” was used to search PubMed, Cochrane Library, Embase, Web of Science, China Biology Medicine (CBM), and China National Knowledge Infrastructure (CNKI) for studies on AI-assisted diagnosis of thyroid nodules based on US images. The summarized receiver operating characteristic (SROC) curve and the pooled sensitivity and specificity were used to assess the performance of the diagnostic tests. The quality assessment of diagnostics accuracy studies-2 (QUADAS-2) tool was used to assess the methodological quality of the included studies. The Review Manager 5.3 and Stata 15 were used to process the data. Subgroup analysis was based on the integrity of data collection. Results. A total of 25 studies with 17,429 US images of thyroid nodules were included. AI-assisted diagnostic techniques had better diagnostic efficacy in the diagnosis of benign and malignant thyroid nodules: sensitivity 0.88 (95% CI: (0.85–0.90)), specificity 0.81 (95% CI: 0.74–0.86), diagnostic odds ratio (DOR) 30 (95% CI: 19–46). The SROC curve indicated that the area under the curve (AUC) was 0.92 (95% CI: 0.89–0.94). Threshold effect analysis showed a Spearman correlation coefficient: 0.17 < 0.5, suggesting no threshold effect for the included studies. After a meta-regression analysis of 4 different subgroups, the results showed a statistically significant effect of mean age ≥50 years on heterogeneity. Compared with studies with an average age of ≥50 years, AI-assisted diagnostic techniques had higher diagnostic performance in studies with an average age of <50 years (0.89 (95% CI: 0.87–0.92) vs. 0.80 (95% CI: 0.73–0.88)), (0.83 (95% CI: 0.77–0.88) vs. 0.73 (95% CI: 0.60–0.87)). Conclusions. AI-assisted diagnostic techniques had good diagnostic efficacy for thyroid nodules. For the diagnosis of <50 year olds, AI-assisted diagnostic technology was more effective in diagnosis.

Objectives. The principal purpose of this meta-analysis was to assess the association between HLA-DRB1 (HLA-DR1, HLA-DR13, and HLA-DR16) polymorphisms and SLE susceptibility. Methods. We searched published case-control studies on the association between HLA-DRB1 polymorphisms and SLE susceptibility from PubMed and Web of Science databases. The pooled ORs with 95% CIs were utilized to estimate the strength of association of HLA-DR1, HLA-DR13, and HLA-DR16 polymorphisms and SLE susceptibility by fixed effect models. We also performed sensitivity analysis, trial sequential analysis, Begg’s test, and Egg’s test in this meta-analysis. Results. A total of 18 studies were included in this meta-analysis. Overall analysis showed that HLA-DR1 and HLA-DR13 polymorphisms were associated with a decreased risk of SLE ( OR = 0.76 , 95% CI: 0.65-0.90, P < 0.01 ; OR = 0.58 , 95% CI: 0.50-0.68, P < 0.01 ), and HLA-DR16 polymorphism was associated with an increased risk of SLE ( OR = 1.70 , 95% CI: 1.24-2.33, P < 0.01 ). In subgroup analysis of ethnicity, the results were as follows: HLA-DR1 polymorphism in Caucasians ( OR = 0.76 , 95% CI: 0.58-0.98, P = 0.04 ) and North Americans ( OR = 0.64 , 95% CI: 0.42-0.96, P = 0.03 ); HLA-DR13 polymorphism in Caucasians ( OR = 0.62 , 95% CI: 0.47-0.82, P < 0.01 ) and East Asians ( OR = 0.44 , 95% CI: 0.34-0.57, P < 0.01 ); and HLA-DR16 polymorphism in East Asians ( OR = 2.62 , 95% CI: 1.71-4.03, P < 0.01 ).Conclusions. This meta-analysis showed that HLA-DR1 and HLA-DR13 are protective factors for SLE, and HLA-DR16 is a risk factor. Due to the limitations of this meta-analysis, the association between HLA-DRB1 polymorphisms and SLE susceptibility needs to be further researched before definitive conclusions are proved.