ArticleAsian biomedicine : research, reviews and news2020
Diagnostic performance of renal ultrasonography in detecting chronic kidney disease of various severity.
Article in Asian biomedicine : research, reviews and news, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Combined application of deep learning and conventional computer vision for kidney ultrasound image classification in chronic kidney disease: preliminary study.Ultrasonography (Seoul, Korea) · 2025Article
- Ureteral reconstruction is safe and successful in poorly functioning kidneys.Frontiers in urology · 2025Article
- Prevalence and prognostic value of baseline and post-treatment renal insufficiency in colorectal cancer patients: a large retrospective cohort study of 10,581 patients in Shanghai, China (2019-2023).Frontiers in medicine · 2025Article
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4 authors.
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Abstract
Background: Association between early diagnosis of chronic kidney disease (CKD) and low morbidity and mortality rate has been proven. Thus, tools for early CKD diagnosis are vital. Ultrasonography has been widely used to diagnose and monitor the progression of CKD. Objectives: To determine the performance of selected renal ultrasonographic parameters for the diagnosis of early CKD. Methods: In a cohort of patients diagnosed with CKD (n = 100), diagnostic performance of ultrasonographically measured renal length (RL), renal cortical thickness (RCT), and parenchymal thickness (PT) was determined using receiver operating curve analysis; correlation of each parameter with the associated comorbidities and serum creatinine (Scr) levels was also determined. Severity of CKD was graded with estimated glomerular filtration rates (eGFR). Results: Of all patient participants, 85 had severity grades 2 or 3. Mean (standard deviation) Scr was 1.88 (0.60) mg/dL; eGFR was 43.3 (11.85) mL/min/1.73 m Conclusion: Index ultrasonic parameters show a diagnostic role in different stages of CKD. The index ultrasound and biochemical parameters showed a complementary role in predicting renal dysfunction.
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