Schwalbe N, Wahl B. The Future of Artificial Intelligence and Global Health. Lancet. 2020; 395:1579–86.
Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H: A systematic review of design, reporting standards, and deep learning research claims. BMJ. 2020; 368: M689.
Chen J, Remulla D, Nguyen JH, Dua A, Liu Y, Dasgupta P, The current status of artificial intelligence applications in other urology and their potential to affect clinical practice. bju int. 2019; 124:567–77.
Shah M, Naik N, Somani BK, Hameed BMZ. Artificial Intelligence (AI) in Urology Use and Future Directions: ITRUE Research. Turk J Urol. 2020; 46: S27 – S39.
Li J, Chong TW, Fong KY, Han Blj, Tan SY, Mui JTS, and other people's engineered intelligence (AI) replaces cytopathologists: scoping reviews of current uses and evidence of AI in urinary cytology. World J-Urol. 2025; 43:200.
Castellani D, De Stefano V, Brocca C, Mazzon G, Celia A, Bosio A, et al. Predictive models based on flexible ureteroscopy (I-FUN) machine learning: a new clinical tool for assessing the risk of retrograde retrograde endosurgical surgery in renal disease. World J-Urol. 2024; 42:612.
Migtistro G, Schott M, Keller P, Tamalunas A, Atzler M, Stief CG, etc. Enucleation and resection: Matched pair analysis of TURP, holep, and bipolar TUEP in medium-sized prostates. Urology. 2021; 154:221–6.
Correlation between transurethral interventions and their effects on the type and duration of postoperative urinary incontinence, including Castellani D, Rubilotta E, Fabiani A, Maggi M, Wroclawski ML, and Teoh Jy-C: Results of a meta-analysis of systematic reviews and comparative studies. J Endourol. 2022; 36:1331–47.
Incidence and risk factors for postoperative urinary incontinence after various prostate nucleation procedures including Hout M, Gurayah A, Arbelaez MCS, Blachman-Braun R, Shah K, Herrmann TRW: A systemic review and meta-analysis of the PubMed literature from 2000 to 2021. 2022; 40:2731–45.
Gauhar V, Castellani D, Herrmann TRW, GökceMI, Fong KY, Gadzhiev N, et al. Incidence of complications and urinary incontinence after endoscopic removal of prostate glands in men with prostate volumes of 80 mL or more: results of multicenter real-world experiences of 2512 patients. World J-Urol. 2024; 42:180.
Gauhar V, Gómez Sancha F, Enikeev D, Sofer M, Fongky, Rodríguez Socarrás M, et al. Endoscopic anatomical removal of the prostate (REAP) by assessing the trends and nuances of prostate excretion in real-world environments resulting from a global multicenter registry of 6193 patients. World J-Urol. 2023; 41:3033–40.
Rokach L. Ensemble-based classifier. Artificial Intelligence Rev. 2010; 33:1–39.
Google Scholar
Langenberger B, Schulte T, Groene O. Applying machine learning to predict high-cost patients: Performance comparisons of different models using healthcare billing data. PLOS 1. 2023; 18: E0279540.
Machine learning, including Luu BC, Wright AL, Haeberle HS, Karnuta JM, Schickendantz MS, Makhni EC, surpasses logistic regression analysis and predicts injuries to NHL players in the upcoming season. An analysis of 2,322 players from 2007 to 2017. 2020; 8:2325967120953404.
Hong W, Zhou X, Jin S, Lu Y, Pan J, Lin Q, et al. Comparison of Xgboost, Random Forest, and Nomographs for the Prediction of Disease Severity in Covid-19 Pneumonia Patients: Implications for Cytokines and Immune Cell Profiles. Microbiol infected with frontier cells. 2022; 12:2022.
Google Scholar
Khalilia M, Chakraborty S, Popescu M. Prediction of disease risk from highly disproportionate data using random forests. BMC Med will notify you of Decis Mak. 2011; 11:51.
Predictors of urinary incontinence after holmium laser enucleation in the Houssin V, Olivier J, Brenier M, Pierache A, Laniado M, Mouton M, et al., et al., prostate gland: a multicenter evaluation. World J-Urol. 2021; 39:143–8.
Cornwell LB, Smith GE, Paonessa JE. Predictors of postoperative urinary incontinence after holmium laser enucleation of the prostate: 12-month follow-up. Urology. 2019; 124:213–7.
Comparative effects of machine learning approaches to predict gastrointestinal bleeding in patients receiving antithrombotic treatment, including Herrin J, Abraham NS, Yao X, Noseworthy PA, Inselman J, and Shah ND. Jama Netw Open. 2021; 4: e2110703-e.
Google Scholar
Wang F. Machine Learning to Predict Rare Clinical Outcomes – Discover needles in haystacks. Jama Netw Open. 2021; 4: e2110738-e.
Google Scholar
Explainable AI for Healthcare 5.0: Opportunities and Challenges. IEEE Access. 2022; 10:84486–517.
Google Scholar
