Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies.

Aims: To systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness. Materials and methods: A search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm. Results: Finally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR. Conclusion: AI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023389687.

Effects of propofol intravenous general anesthesia and inhalational anesthesia on T-lymphocyte activity after breast cancer surgery: A meta-analysis.

Background: Breast cancer is one of the most common cancers in women. General anesthesia is a commonly used anesthesia method for breast cancer surgery, and studies have confirmed that general anesthesia can induce immunosuppression in breast cancer patients and increase the metastasis rate of tumors. However, the difference between the effects of intravenous general anesthesia and inhalation anesthesia on the function of T-lymphocytes is still controversial, and it is necessary to explore reasonable anesthesia methods to reduce immunosuppression caused by surgery and anesthesia. Materials and Methods: Databases (Embase, PubMed, Cochrane Library, CBM, CNKI, and Wanfang) were searched (up to October 2022) for randomized controlled trials (RCTs) comparing intraoperative inhalation anesthesia and propofol intravenous anesthesia in breast cancer patients, with the outcome of T-lymphocyte subsets. The meta-analysis was performed by STATA 14.0. Results: Six RCTs with 352 patients were included in the study. Compared with inhalation anesthesia, there was no difference in T-lymphocyte subsets between the two groups immediately after surgery, but the activities of CD4+ T cells in patients with propofol anesthesia were higher (standard mean difference [SMD] = 0.234, 95% confidence interval [CI]: 0.003-0.466, P = 0.047, I2 = 44.1%) than those under inhalation anesthesia 1 day after surgery, and CD4+/CD8+ activities in patients with propofol anesthesia were higher (SMD = 304, 95% CI: 0.072-0.537, P = 0.010, I2 = 48.0%) than those under inhalation anesthesia 1 day after surgery. Conclusion: There were no differences in the effects of propofol and inhalation anesthetics on T-lymphocytes immediately after surgery, but the inhibitory effects of inhalation anesthetics on CD4+ and CD4+/CD8+ cells were stronger 1 day after surgery.