Machine learning prediction models for diabetic kidney disease: A systematic review and meta-analysis.

BACKGROUND

Machine learning (ML) has emerged as a promising tool for predicting diabetic kidney disease (DKD), yet the performance and clinical utility of these models remain unclear. We conducted a systematic review and meta-analysis to evaluate ML models for DKD prediction.

METHODS

We systematically searched seven databases from inception to May 16, 2026. Studies developing or validating ML models for DKD diagnosis were included. Quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) and its AI extension (PROBAST+AI), and reporting completeness was evaluated using the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement and its AI extension (TRIPOD+AI). Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using bivariate random-effects models.

RESULTS

Sixty-six models were included. The pooled AUC was 0.926 (95% CI: 0.898-0.947), with sensitivity of 0.83 (95% CI: 0.79-0.86) and specificity of 0.90 (95% CI: 0.87-0.93). Neural networks achieved the highest AUC (0.973), followed by random forest (0.910). However, heterogeneity was substantial (I2 > 85%). Meta-regression identified model type (P = 0.019) and study design (P = 0.037) as significant effect modifiers.

CONCLUSIONS

ML models show promising discriminative performance for DKD prediction, but the evidence is severely limited by high heterogeneity, lack of external validation and reporting of calibration metrics, and poor methodological transparency. Future research must prioritize external validation, calibration assessment, standardized reporting, and prospective impact studies.