Optimizing Renal Fibrosis Assessment in Patients with Chronic Kidney Disease: Feature and Decision-Level Fusion in Machine Learning Using Clinical and Ultrasound Information

Abstract

Purpose: Machine learning has been extensively applied in nephrology. This study aims to evaluate and compare the effectiveness of various information fusion strategies for differentiating mild from moderate-to-severe renal fibrosis in patients with chronic kidney disease (CKD). Methods: This prospective study enrolled CKD patients who underwent renal ultrasound and biopsy at our institution from April 2019 to June 2022. Clinical laboratory indicators and ultrasound parameters were collected and analyzed. Two fusion strategies using machine learning techniques were developed: one at the feature level, which combines clinical and ultrasound features, and the other at the decision level, which integrates decisions from individual modality model outputs. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC), along with sensitivity, specificity, and accuracy metrics. Results: The multimodality fusion strategy demonstrated enhanced diagnostic performance compared to the single modality models. In the test cohort, the decision-level fusion model yielded optimal diagnostic performance, with an AUC of 0.92 (95% CI: 0.84–0.99), sensitivity of 0.80, specificity of 0.90, and accuracy of 0.84. This was followed by the feature-level fusion model (AUC: 0.86; 95% CI: 0.76–0.95), the clinical model (AUC: 0.82; 95% CI: 0.71–0.92), and the ultrasound model (AUC: 0.80; 95% CI: 0.68–0.93). Conclusion: Information fusion strategies enhance diagnostic performance in differentiating mild from moderate-to-severe renal fibrosis in CKD patients. The decision-level fusion approach, which integrates outputs from individual models, proved most effective. These advancements support personalized CKD management by facilitating risk stratification and targeted treatment approaches.