Multimodal Imaging Biomarkers and Artificial Intelligence–Assisted Early Prediction of Acute Kidney Injury in Critically Ill Patients: Emerging Frontiers in Precision Nephro-Critical Care
Acute kidney injury; critical care; multimodal imaging; ultrasound; magnetic resonance imaging; artificial intelligence; machine learning; early prediction; renal perfusion; precision medicine.
AuthorsAbstractAcute kidney injury (AKI) remains a major cause of morbidity and mortality among critically ill patients, affecting nearly half of those admitted to intensive care units and contributing to prolonged hospitalization, multiorgan dysfunction, and increased long-term risk of chronic kidney disease. Despite advances in supportive care, clinical recognition of AKI continues to rely heavily on delayed surrogates such as serum creatinine and urine output, which inadequately reflect the underlying dynamics of renal injury and repair. This temporal disconnect has galvanized interest in noninvasive imaging biomarkers capable of detecting subclinical renal alterations well before functional decline becomes apparent. Recent developments in renal ultrasound, particularly contrast-enhanced and Doppler-based techniques, allow real-time assessment of cortical perfusion, microvascular resistance, and renal tissue elasticity. Magnetic resonance imaging (MRI) modalities, including blood oxygen level–dependent imaging, diffusion tensor imaging, and arterial spin labeling, have provided novel insights into oxygenation and microstructural changes underlying AKI pathophysiology. When integrated into a multimodal framework, these techniques can quantify both macro- and microcirculatory disturbances, offering a mechanistic bridge between hemodynamic instability and cellular injury. Concurrently, artificial intelligence (AI) and machine learning algorithms are transforming early AKI prediction through data fusion approaches that integrate imaging-derived metrics with clinical, hemodynamic, and biochemical variables. These AI-driven platforms have shown promise in detecting subclinical injury patterns and forecasting AKI trajectories, enabling individualized prevention and timely intervention. The convergence of advanced imaging and predictive analytics signals a new era of precision critical care, where clinicians may soon transition from reactive diagnosis to proactive organ protection. Future success will depend on harmonizing imaging protocols, validating AI models across diverse patient populations, and integrating these technologies seamlessly into critical care workflows to improve renal outcomes. •••••••••••••••••••••••••••••••• ejprd.org- Published by Riset Publishing Services LLC.
EJPRD
Copyright © 2026 by Riset Publishing Services LLC