The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis

Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90–0.91), specificity was 0.91 (95% CI, 0.90–0.92) and the AUC was 0.96 (95% CI, 0.91–0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.88 (95% CI, 0.87–0.88) and the AUC was 0.96 (95% CI, 0.93–0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.95 (95% CI, 0.94–0.95) and the AUC was 0.97 (95% CI, 0.96–0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies

Diagnostic Accuracy of Serum and Urine S100A8/A9 and Serum Amyloid A in Probable Acute Abdominal Pain at Emergency Department

Study Design. This study was performed to investigate the diagnostic values of some inflammatory biomarkers in abdominal pain. Methods. Patients over 18 years of age with acute recent abdominal pain who presented to the Emergency Department were evaluated. Serum and urinary samples were taken and evaluated for serum and urine S100A8/A9 and serum amyloid A. All patients were referred to a surgeon and were followed up until the final diagnosis. In the end, the final diagnosis was compared with the levels of biomarkers. Results. Of a total of 181 patients, 71 underwent surgery and 110 patients did not need surgery after they were clinically diagnosed. Mean levels of serum and urine S100A8/A9 had a significant difference between two groups, but serum amyloid A did not show. The diagnostic accuracy of serum S100A8/A9, urine S100A8/A9, and serum amyloid A was 86%, 79%, and 50%, respectively, in anticipation of the need or no need for surgery in acute abdominal pain. Conclusions. Our study showed that in acute abdominal pain, serum and urine S100A8/A9 can be useful indicators of the need for surgery, but serum amyloid A had a low and nonsignificant diagnostic accuracy