Mayans L, Nephrolithiasis (2019) Prim Care 46:203–212

Article  PubMed  Google Scholar 

Bishop K, Momah T, Ricks J, Nephrolithiasis (2020) Prim Care 47:661–671

Article  PubMed  Google Scholar 

Pfau A, Knauf F (2016) Update on nephrolithiasis: core curriculum 2016. Am J Kidney Dis 68:973–985

Article  PubMed  Google Scholar 

Siener R (2021) Nutrition and kidney stone disease. Nutrients. ; 13

Sakhaee K, Maalouf NM, Sinnott B (2012) Clinical review. Kidney stones 2012: pathogenesis, diagnosis, and management. J Clin Endocrinol Metab 97:1847–1860

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mandeville JA, Gnessin E, Lingeman JE (2011) Imaging evaluation in the patient with renal stone disease. Semin Nephrol 31:254–258

Article  PubMed  Google Scholar 

Shoag J, Tasian GE, Goldfarb DS et al (2015) The new epidemiology of nephrolithiasis. Adv Chronic Kidney Dis 22:273–278

Article  PubMed  Google Scholar 

Ingimarsson JP, Krambeck AE, Pais VJ (2016) Diagnosis and management of nephrolithiasis. Surg Clin North Am 96:517–532

Article  PubMed  Google Scholar 

Tan YK, Cha DY, Gupta M (2013) Management of stones in abnormal situations. Urol Clin North Am 40:79–97

Article  PubMed  Google Scholar 

Ma C, Wang X, Wu J et al (2020) Real-world big-data studies in laboratory medicine: current status, application, and future considerations. Clin Biochem 84:21–30

Article  CAS  PubMed  Google Scholar 

Chen ZH, Lin L, Wu CF et al (2021) Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond) 41:1100–1115

Article  PubMed  Google Scholar 

Yang HS, Wang F, Greenblatt MB et al (2023) Ai chatbots in clinical laboratory medicine: foundations and trends. Clin Chem 69:1238–1246

Article  PubMed  Google Scholar 

Bellini V, Valente M, Gaddi AV et al (2022) Artificial intelligence and telemedicine in anesthesia: potential and problems. Minerva Anestesiol 88:729–734

Article  PubMed  Google Scholar 

Zheng J, Yu H, Batur J et al (2021) A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int 100:870–880

Article  PubMed  Google Scholar 

Hong X, Liu G, Chi Z et al (2023) Predictive model for urosepsis in patients with upper urinary tract calculi based on ultrasonography and urinalysis using artificial intelligence learning. Int Braz J Urol 49:221–232

Article  PubMed  Google Scholar 

Xiang L, Jin X, Liu Y et al (2022) Prediction of the occurrence of calcium oxalate kidney stones based on clinical and gut microbiota characteristics. World J Urol 40:221–227

Article  CAS  PubMed  Google Scholar 

Eun SJ, Yun MS, Whangbo TK et al (2022) A study on the optimal artificial intelligence model for determination of urolithiasis. Int Neurourol J 26:210–218

Article  PubMed  PubMed Central  Google Scholar 

Kazemi Y, Mirroshandel SA (2018) A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med 84:117–126

Article  PubMed  Google Scholar 

Kavoussi NL, Floyd C, Abraham A et al (2022) Machine learning models to predict 24 hour urinary abnormalities for kidney stone disease. Urology 169:52–57

Article  PubMed  Google Scholar 

Elton DC, Turkbey EB, Pickhardt PJ et al (2022) A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast ct scans. Med Phys 49:2545–2554

Article  PubMed  Google Scholar 

Sassanarakkit S, Hadpech S, Thongboonkerd V (2023) Theranostic roles of machine learning in clinical management of kidney stone disease. Comput Struct Biotechnol J 21:260–266

Article  PubMed  Google Scholar 

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

Article  CAS  PubMed  Google Scholar 

Chen Z, Ma M, Li T et al (2023) Long sequence time-series forecasting with deep learning: a survey. Inf Fusion 97:101819

Article  Google Scholar 

Dupond S (2019) A thorough review on the current advance of neural network structures. Annu Rev Control 14:200–230

Google Scholar 

Mao C, Zhu Q, Chen R et al (2023) Automatic medical specialty classification based on patients’ description of their symptoms. Bmc Med Inf Decis Mak 23:1–9

Google Scholar 

Wu YX, Wu QB, Zhu JQ (2019) Data-driven wind speed forecasting using deep feature extraction and lstm. Iet Renew Power Gener 13:2062–2069

Article  Google Scholar 

Naeem H, Bin-Salem AA (2021) A cnn-lstm network with multi-level feature extraction-based approach for automated detection of coronavirus from ct scan and x-ray images. Appl Soft Comput 113:107918

Article  PubMed  PubMed Central  Google Scholar 

Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. pmlr

Eckle K, Schmidt-Hieber J (2019) A comparison of deep networks with relu activation function and linear spline-type methods. Neural Netw 110:232–242

Article  PubMed  Google Scholar 

Su J, Liu Z, Zhang J et al (2021) Dv-net: accurate liver vessel segmentation via dense connection model with d-bce loss function. Knowl Based Syst 232:107471

Article  Google Scholar 

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

Ranka S, Mohan CK, Mehrotra K et al (1996) Characterization of a class of sigmoid functions with applications to neural networks. Neural Netw 9:819–835

Article  PubMed  Google Scholar 

Zeng G, Mai Z, Xia S et al (2017) Prevalence of kidney stones in China: an ultrasonography based cross-sectional study. Bju Int 120:109–116

Article  PubMed  Google Scholar 

Peng JP, Zheng H (2017) Kidney stones may increase the risk of coronary heart disease and stroke: a prisma-compliant meta-analysis. Med (Baltim) 96:e7898

Article  CAS  Google Scholar 

Sarkar C, Das B, Rawat VS et al (2023) Artificial intelligence and machine learning technology driven modern drug discovery and development. Int J Mol Sci. ; 24

Theodosiou AA, Read RC (2023) Artificial intelligence, machine learning and deep learning: potential resources for the infection clinician. J Infect 87:287–294

Article  PubMed  Google Scholar 

Merkin A, Krishnamurthi R, Medvedev ON (2022) Machine learning, artificial intelligence and the prediction of dementia. Curr Opin Psychiatry 35:123–129

Article  PubMed  Google Scholar 

De Perrot T, Hofmeister J, Burgermeister S et al (2019) Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning. Eur Radiol 29:4776–4782

Article  PubMed  Google Scholar 

Sudharson S, Kokil P (2021) Computer-aided diagnosis system for the classification of multi-class kidney abnormalities in the noisy ultrasound images. Comput Methods Programs Biomed 205:106071

Article  CAS  PubMed  Google Scholar 

Abraham A, Kavoussi NL, Sui W et al (2021) Machine learning prediction of kidney stone composition using electronic health record-derived features. J Endourol 36:243–250

Article  Google Scholar 

Bergsland KJ, Coe FL, El-Achkar TM et al (2021) Increased urinary leukocyte esterase distinguishes patients with brushite kidney stones. Kidney Int Rep 6:1729–1731

Article  PubMed  PubMed Central