Machine learning and deep learning in predicting the risk of diabetic kidney disease: A systematic review and meta-analysis

BACKGROUND Machine learning (ML) and deep learning (DL) algorithms have been utilised to predict the risk of diabetic kidney disease (DKD) in individuals with type 2 diabetes mellitus (T2DM). Despite promising results, concerns regarding the clinical applicability and performance of these artificial intelligence models remain. AIM To evaluates the performance of ML-based and DL-based models in predicting DKD risk among T2DM patients. METHODS A systematic search was conducted across five databases. The risk of bias was assessed using the prediction model risk of bias assessment tool. After data extraction, summary point estimates of the area under the receiver operating characteristic curve (AUC) were aggregated. Heterogeneity was evaluated with the I 2 statistic and Cochrane Q test, and subgroup analyses were performed to identify potential sources of heterogeneity. RESULTS Twelve eligible studies were included. The pooled AUC for the top-performing artificial intelligence models was 0.858 [95% confidence interval (CI): 0.779-0.912], with a prediction interval of 0.480-0.975. Significant heterogeneity was detected (I 2 = 99.7%). Studies employing cross-validation methods demonstrated significantly higher diagnostic accuracy (pooled AUC = 0.88; 95%CI: 0.79-0.94) compared to those using simple holdout validation (pooled AUC = 0.77; 95%CI: 0.59-0.89, P = 0.0231). Predictive factors most frequently used for DKD prediction included age, body mass index, estimated glomerular filtration rate, serum creatinine, urinary albumin, glycated hemoglobin, systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides. CONCLUSION ML and DL algorithms exhibit strong performance in predicting DKD in patients with T2DM. However, future research should focus on standardizing model development and validation processes.