ReviewComputational and structural biotechnology journal2023
Theranostic roles of machine learning in clinical management of kidney stone disease.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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Who cites it
9 citing papers in PubMed, 23 citations in OpenAlex.
- Construction of a LASSO regression-based predictive model for recurrence of urinary tract stones.Translational andrology and urology · 2026Article
- Systematic evaluation and comparison of theCurrent research in food science · 2026Article
- Effects of calcium oxalate crystals on neutrophil cellular proteome and functions: implications for nephrolithiasis.Cell communication and signaling : CCS · 2025Article
- Epidemiology and management of urological emergencies in a tertiary care setting in Scandinavia.International journal of emergency medicine · 2025Article
- Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.Scientific reports · 2025Article
- The SHA.LIN renal stone scoring system for predicting stone-free status and postoperative outcomes after percutaneous nephrolithotomy.Frontiers in surgery · 2025Article
- Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments.Urolithiasis · 2024Article
- Automatic kidney stone identification: an adaptive feature-weighted LSTM model based on urine and blood routine analysis.Urolithiasis · 2024Article
- Application of visual transformer in renal image analysis.Biomedical engineering online · 2024Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome.
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Registered trials
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