Non-invasive assessment techniques for renal fibrosis: advances and perspectives

Renal fibrosis is a critical pathological process driving chronic kidney disease (CKD) and end-stage renal disease (ESRD). Early diagnosis is essential for timely intervention, yet traditional methods like renal biopsy are invasive and present significant limitations. Consequently, noninvasive techniques are gaining attention for renal fibrosis assessment. Biomarkers such as transforming growth factor beta 1 (TGF-β1) and platelet-derived growth factor D (PDGF-D) have been explored for monitoring fibrosis progression, though their specificity and reliability challenges persist. Emerging imaging techniques, including molecular and functional imaging, offer valuable structural and functional insights but remain limited in detecting early-stage fibrosis. Artificial intelligence (AI) has emerged as a promising tool to enhance diagnostic accuracy by integrating imaging and biomarker data. Machine learning algorithms applied to ultrasound, computed tomography, and magnetic resonance imaging have demonstrated improved predictive capabilities for renal fibrosis detection. Furthermore, AI-driven multimodal approaches, combining clinical, imaging, and biomarker data, provide new opportunities for accurate, noninvasive diagnosis and monitoring. Despite these advancements, challenges such as small sample sizes, lack of standardization, and AI model interpretability must be addressed. Future research should focus on refining noninvasive biomarkers, improving imaging techniques, and developing AI-driven models to enhance diagnostic accuracy and clinical applicability in renal fibrosis assessment. This review provides a comprehensive overview of recent advancements in noninvasive approaches for detecting renal fibrosis, highlighting their potential to advance clinical decision-making and ultimately benefiting CKD management and patient outcomes.