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INTRODUCTION: The commonly used clinical indicators are not sensitive and comprehensive enough to evaluate the early staging of chronic kidney disease (CKD). This study aimed to evaluate the differences in arterial spin labeling (ASL) and blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-MRI) parameter values among patients at various stages of chronic kidney disease and healthy individuals. METHODS: Electronic databases PubMed, Web of Science, Cochrane, and Embase were searched from inception to March 29, 2024, to identify relevant studies on ASL and BOLD in CKD. The renal blood flow (RBF) and apparent relaxation rate (R2*) values were obtained from healthy individuals and patients with various stages of CKD. The meta-analysis was conducted using STATA version 12.0. The random-effects model was used to obtain estimates of the effects, and the results were expressed as 95% confidence intervals (CIs) and mean differences (MDs) of continuous variables. RESULTS: A total of 18 published studies were included in this meta-analysis. The cortical RBF and R2* values and medulla RBF values were considerably distinct between patients with various stages of CKD and healthy controls (MD, - 78.162; 95% CI, - 85.103 to - 71.221; MD, 2.440; 95% CI, 1.843 to 3.037; and MD, - 36.787; 95% CI, - 47.107 to - 26.468, respectively). No obvious difference in medulla R2* values was noted between patients with various stages of CKD and healthy controls (MD, - 1.475; 95% CI, - 4.646 to 1.696). CONCLUSION: ASL and BOLD may provide complementary and distinct information regarding renal function and could potentially be used together to gain a more comprehensive understanding of renal physiology.
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Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.