Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis

Chronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown the potential to improve the prediction of CKD progression, offering increased accuracy over traditional methods. Therefore, this systematic review and meta-analysis examine the diagnostic performance of various AI models in predicting CKD. Search was performed in different databases for studies reporting the diagnostic accuracy of AI-based prediction models for the progression of CKD. Meanwhile, pre-defined eligibility criteria were used for the selection of studies. Pooled sensitivity, specificity, and area under curve (AUC) were calculated utilizing Meta-disc 1.4. Quality assessment was performed using the prediction model risk of bias assessment tool (PROBAST). A total of 33 studies were included. The pooled sensitivity of prediction tools was 0.43 (95% CI, 0.41–0.44, <i>I²</i> = 99.3%, <i>p</i> &lt; 0.01). A significant difference (<i>p</i> &lt; 0.01) was also observed in the pooled specificity 0.92 (95% CI, 0.91–0.92, <i>I²</i> = 99.5%). Positive likelihood ratio (PLP) and negative likelihood ratio (NLR) were 5.12 (95% CI: 3.60–7.27, <i>I²</i> = 91.3%, <i>p</i> &lt; 0.01) and 0.28 (95% CI: 0.21–0.37, <i>I²</i> = 99.3%, <i>p</i> &lt; 0.01), respectively and AUC was 0.89, suggesting a diagnostic accuracy of AI-based prediction models for the progression of CKD. This study demonstrates the promising potential of AI models in predicting CKD progression. However, further efforts are needed to optimize model performance, particularly in balancing sensitivity and specificity to ensure generalizability across diverse populations. Limitations of this study include the potential for overfitting in certain AI models due to imbalanced datasets. The high heterogeneity and the lack of standardized predictors limit the generalizability of findings across different populations.