Diagnostic Accuracy of Ai-Augmented Renal Ultrasound for Degenerative Kidney Disorders: A Systematic Review and Meta-Analysis Publisher

Summary: Can AI improve kidney ultrasound assessment? A meta-analysis found promising accuracy, but inconsistent studies and scarce external testing limit clinical readiness. #MedicalAI #Ultrasound

Elhaie M ; Koozari A ; Nasab A S ; Dehaghi F T
Source: Future Science OA Published:2026

Abstract

Introduction: This systematic review and meta-analysis evaluated artificial intelligence (AI) and radiomics applied to renal ultrasound for the diagnosis, staging, and prognosis of degenerative kidney disorders. Methods: PubMed/MEDLINE, Embase, Scopus, Web of Science, the Cochrane Library, and gray-literature sources were searched without date or language restrictions. Eligible studies applied AI or radiomics to renal ultrasound and reported diagnostic, staging, or prognostic outcomes. Risk of bias and methodological quality were assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST), Checklist for Artificial Intelligence in Medical Imaging (CLAIM 2024), and Radiomics Quality Score 2.0 (RQS 2.0). Random-effects models were used for diagnostic accuracy synthesis. Results: Thirty-one studies were included. Machine-learning models achieved pooled sensitivity of 0.86 (95% confidence interval 0.82–0.90) and specificity of 0.83 (0.79–0.87); deep-learning models achieved sensitivity of 0.89 (0.84–0.93) and specificity of 0.85 (0.81–0.91). Heterogeneity was substantial and external validation was uncommon. Conclusions: AI-augmented renal ultrasound shows promising diagnostic performance, but heterogeneous populations, limited calibration, and predominantly internal validation constrain clinical generalizability. Prospective multicenter external validation is required. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.