ReviewFuture science OA2026
Diagnostic accuracy of AI-augmented renal ultrasound for degenerative kidney disorders: a systematic review and meta-analysis.
Review in Future science OA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThis 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.
methodsPubMed/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.
resultsThirty-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.
conclusionsAI-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.