RESUMO
Introduction: Small bowel obstruction (SBO) is known as a common cause of acute abdominal complaints in the emergency department (ED). The modality of choice for the diagnosis of SBO has not yet been established. This systematic review and meta-analysis aimed to investigate the accuracy of ultrasonography for the diagnosis of SBO. Methods: Systematic search was performed on five electronic databases including Medline, Scopus, Web of Sciences, Embase, and Cochrane Library, and the retrieval period was from the inception of each database to November 2023. The quality of the included studies were investigated using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The pooled values of diagnostic characteristics for ultrasonography were estimated using meta-Disc and Stata statistical software. Results: Twenty-one studies with a total of 1977 patients were included in the meta-analysis. The pooled estimate for sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary ROC curve of ultrasonography for diagnosing SBO were 0.93 (95% CI: 0.91-0.95), 0.8 (95% CI: 0.77-0.83), 5.69 (95% CI: 3.64-8.89), 0.1 (95% CI: 0.07-0.16), 83.51 (95% CI: 18.12-182.91) and 0.96, respectively. Conclusion: The findings of this meta-analysis showed that the utilization of ultrasonography holds promise as a diagnostic imaging for SBO with high accuracy. However, additional worldwide studies are essential to get more evidence on the value of ultrasonography for the diagnosis of SBO.
RESUMO
Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method.