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References from Theranostic roles of machine learning in clinical management of kidney stone disease. Local targets link to admitted publications; unresolved targets remain external evidence.
Kidney stones: treatment and prevention
2019 · External reference
Management of kidney stones in 2020
10.1001/jama.2020.0662 · 2020 · External reference
Kidney stone disease: an update on its management in primary care
10.3399/bjgp20x709277 · 2020 · External reference
Is there a place for extracorporeal shockwave lithotripsy (ESWL) in the endoscopic era?
10.1007/s00240-022-01307-4 · 2022 · External reference
A population-based, retrospective cohort study analyzing contemporary trends in the surgical management of urinary stone disease in adults
2022 · External reference
Imaging in stone diagnosis and surgical planning
10.1097/mou.0000000000001002 · 2022 · External reference
Risk factors for kidney stone disease recurrence: a comprehensive meta-analysis
10.1186/s12894-022-01017-4 · 2022 · External reference
Clinician versus nomogram predicted estimates of kidney stone recurrence risk
10.1089/end.2020.0978 · 2021 · External reference
10.14740/jocmr3753
10.14740/jocmr3753 · External reference
10.1016/j.jclinepi.2021.11.023
10.1016/j.jclinepi.2021.11.023 · External reference
Using modern risk engines and machine learning/artificial intelligence to predict diabetes complications: A focus on the BRAVO model
10.1016/j.jdiacomp.2022.108316 · 2022 · External reference
10.1177/17562872211044880
10.1177/17562872211044880 · External reference
10.3390/jcm10091864
10.3390/jcm10091864 · External reference
The ascent of artificial intelligence in endourology: a systematic review over the last 2 decades
10.1007/s11934-021-01069-3 · 2021 · External reference
10.1016/j.media.2021.101960
10.1016/j.media.2021.101960 · External reference
Automated prediction of kidney failure in IgA nephropathy with deep learning from biopsy images
2022 · External reference
Using machine learning to identify patients at high risk of inappropriate drug dosing in periods with renal dysfunction
10.2147/clep.s344435 · 2022 · External reference
Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning
10.1038/s41598-019-48263-5 · 2019 · External reference
Use of a neural network to predict stone growth after shock wave lithotripsy
10.1016/s0090-4295(97)00611-0 · 1998 · External reference
Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization
10.1148/ryai.2019180066 · 2019 · External reference
Deep learning computer vision algorithm for detecting kidney stone composition
10.1111/bju.15035 · 2020 · External reference
Predicting the postoperative outcome of percutaneous nephrolithotomy with machine learning system: software validation and comparative analysis with guy's stone score and the CROES nomogram
10.1089/end.2019.0475 · 2020 · External reference
10.1155/2018/3068365
10.1155/2018/3068365 · External reference
The history of urinary stones: in parallel with civilization
10.1155/2013/423964 · 2013 · External reference
Proteomics and kidney stone disease
10.1159/000125972 · 2008 · External reference
Epidemiology of stone disease across the world
10.1007/s00345-017-2008-6 · 2017 · External reference
Assessment of the impact of geogenic and climatic factors on global risk of urinary stone disease
10.1016/j.scitotenv.2020.137769 · 2020 · External reference
A nomogram for the prediction of kidney stone recurrence
10.1681/asn.2014060631 · 2014 · External reference
Trends in the prevalence of kidney stones in the United States from 2007 to 2016
10.1007/s00240-020-01210-w · 2021 · External reference
Prevalence and trends in kidney stone among adults in the USA: analyses of national health and nutrition examination survey 2007–2018 Data
10.1016/j.euf.2020.08.011 · 2021 · External reference
Urinary stone epidemiology in Spain and worldwide
2021 · External reference
Recent advances on the mechanisms of kidney stone formation (Review)
10.3892/ijmm.2021.4982 · 2021 · External reference
Urinary stone composition in Germany: results from 45,783 stone analyses
10.1007/s00345-022-04060-w · 2022 · External reference
Trends in urinary stone composition in 23,182 stone analyses from 2011 to 2019: a high-volume center study in China
10.1007/s00345-021-03680-y · 2021 · External reference
Timelines of the “free-particle” and “fixed-particle” models of stone-formation: theoretical and experimental investigations
10.1007/s00240-016-0946-x · 2017 · External reference
Physicochemical mechanisms of stone formation
10.1007/s00240-016-0942-1 · 2017 · External reference
Morphological characteristics and microstructure of kidney stones using synchrotron radiation muCT reveal the mechanism of crystal growth and aggregation in mixed stones
10.1371/journal.pone.0214003 · 2019 · External reference
Defining and systematic analyses of aggregation indices to evaluate degree of calcium oxalate crystal aggregation
10.3389/fchem.2017.00113 · 2017 · External reference
10.1021/pr2006878
10.1021/pr2006878 · External reference
Kidney stones
10.1038/nrdp.2016.8 · 2016 · External reference
Histological aspects of the “fixed-particle” model of stone formation: animal studies
10.1007/s00240-016-0949-7 · 2017 · External reference
The origin and growth of renal calculi
10.1097/00000658-193706000-00014 · 1937 · External reference
Randall's plaque and calcium oxalate stone formation: role for immunity and inflammation
10.1038/s41581-020-00392-1 · 2021 · External reference
Randall's plaque in stone formers originates in ascending thin limbs
10.1152/ajprenal.00035.2018 · 2018 · External reference
The relationship between the severity of pain and stone size, hydronephrosis and laboratory parameters in renal colic attack
10.1016/j.ajem.2019.06.013 · 2019 · External reference
An overview of kidney stone imaging techniques
10.1038/nrurol.2016.154 · 2016 · External reference
Guideline of the guidelines: urolithiasis
10.1097/mou.0000000000000855 · 2021 · External reference
Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning
10.1007/s00330-019-6004-7 · 2019 · External reference
Deep segmentation networks for segmenting kidneys and detecting kidney stones in unenhanced abdominal CT images
10.3390/diagnostics12081788 · 2022 · External reference
Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography
10.1038/s41598-022-15634-4 · 2022 · External reference
Deep learning model-assisted detection of kidney stones on computed tomography
10.1590/s1677-5538.ibju.2022.0132 · 2022 · External reference
Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks
10.1016/j.compbiomed.2018.04.021 · 2018 · External reference
Computer-aided diagnosis system for the classification of multi-class kidney abnormalities in the noisy ultrasound images
10.1016/j.cmpb.2021.106071 · 2021 · External reference
Deep learning model for automated kidney stone detection using coronal CT images
10.1016/j.compbiomed.2021.104569 · 2021 · External reference
automatic detection and scoring of kidney stones on noncontrast CT images using S.T.O.N.E. nephrolithometry: combined deep learning and thresholding methods
10.1007/s11307-020-01554-0 · 2021 · External reference
A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans
10.1002/mp.15518 · 2022 · External reference
Automated machine learning segmentation and measurement of urinary stones on CT scan
10.1016/j.urology.2022.07.029 · 2022 · External reference
Prediction of the occurrence of calcium oxalate kidney stones based on clinical and gut microbiota characteristics
10.1007/s00345-021-03801-7 · 2022 · External reference
Machine learning models to predict 24 hour urinary abnormalities for kidney stone disease
2022 · External reference
Performance of a natural language processing method to extract stone composition from the electronic health record
10.1016/j.urology.2019.07.007 · 2019 · External reference
A novel method for predicting kidney stone type using ensemble learning
10.1016/j.artmed.2017.12.001 · 2018 · External reference
Machine learning prediction of kidney stone composition using electronic health record-derived features
10.1089/end.2021.0211 · 2022 · External reference
Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm
10.1016/j.compbiomed.2019.103366 · 2019 · External reference
Prediction of the uric acid component in nephrolithiasis using simple clinical information about metabolic disorder and obesity: a machine learning-based model
10.3390/nu14091829 · 2022 · External reference
Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study
10.1007/s00330-019-06455-7 · 2020 · External reference
A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning
10.1016/j.kint.2021.05.031 · 2021 · External reference
Evaluation and understanding of automated urinary stone recognition methods
10.1111/bju.15767 · 2022 · External reference
Assessing kidney stone composition using smartphone microscopy and deep neural networks
10.1002/bco2.137 · 2022 · External reference
Deep morphological recognition of kidney stones using intra-operative endoscopic digital videos
10.1088/1361-6560/ac8592 · 2022 · External reference
External validation of outcome prediction model for ureteral/renal calculi
10.1016/s0022-5347(05)00244-2 · 2006 · External reference
A neural network - based algorithm for predicting stone - free status after ESWL therapy
10.1590/s1677-5538.ibju.2016.0630 · 2017 · External reference
Prediction of lower pole stone clearance after shock wave lithotripsy using an artificial neural network
10.1097/01.ju.0000055624.65386.b9 · 2003 · External reference
Can we improve the prediction of stone-free status after extracorporeal shock wave lithotripsy for ureteral stones? A neural network or a statistical model?
10.1097/01.ju.0000128646.20349.27 · 2004 · External reference
Three-dimensional texture analysis with machine learning provides incremental predictive information for successful shock wave lithotripsy in patients with kidney stones
10.1016/j.juro.2018.04.059 · 2018 · External reference
Prediction of fragmentation of kidney stones: A statistical approach from NCCT images
10.5489/cuaj.3674 · 2016 · External reference
A Prediction model using machine learning algorithm for assessing stone-free status after single session shock wave lithotripsy to treat ureteral stones
10.1016/j.juro.2018.06.077 · 2018 · External reference
Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy
10.1186/s12894-020-00662-x · 2020 · External reference
A machine learning approach to predict the outcome of urinary calculi treatment using shock wave lithotripsy: model development and validation study
10.2196/33357 · 2022 · External reference
Artificial neural network system to predict the postoperative outcome of percutaneous nephrolithotomy
10.1089/end.2016.0791 · 2017 · External reference
An artificial intelligence-based clinical decision support system for large kidney stone treatment
10.1007/s13246-019-00780-3 · 2019 · External reference
Application of artificial intelligence-based classifiers to predict the outcome measures and stone-free status following percutaneous nephrolithotomy for staghorn calculi: cross-validation of data and estimation of accuracy
10.1089/end.2020.1136 · 2021 · External reference
