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Aksakalli T, Aksakalli IK, Cinislioglu AE, Utlu A, Demirdogen SO, Celik F, Karabulut I. Prediction of spontaneous distal ureteral stone passage using artificial intelligence. Int Urol Nephrol 2024:10.1007/s11255-024-03955-4. [PMID: 38340263 DOI: 10.1007/s11255-024-03955-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 01/06/2024] [Indexed: 02/12/2024]
Abstract
PURPOSE Identifying factors predicting the spontaneous passage of distal ureteral stones and evaluating the effectiveness of artificial intelligence in prediction. MATERIALS AND METHODS The files of patients presenting with distal ureteral stones were retrospectively evaluated. Those who experienced spontaneous passage were assigned to Group P, while those who did not were assigned to Group N. Demographic and clinical data of both groups were compared. Then, logistic regression analysis was performed to determine the factors predicting spontaneous stone passage. Based on these factors, a logistic regression model was prepared, and artificial intelligence algorithms trained on the dataset were compared with this model to evaluate the effectiveness of artificial intelligence in predicting spontaneous stone passage. RESULTS When comparing stone characteristics and NCCT findings, it was found that the stone size was significantly smaller in Group P (4.9 ± 1.7 mm vs. 6.8 ± 1.4 mm), while the ureteral diameter was significantly higher in Group P (3.3 ± 0.9 mm vs. 3.1 ± 1.1 mm) (p < 0.05). Parameters such as stone HU, stone radiopacity, renal pelvis AP diameter, and perirenal stranding were similar between the groups. In multivariate analysis, stone size and alpha-blocker usage were significant factors in predicting spontaneous stone passage. The ROC analysis for the logistic regression model constructed from the significant variables revealed an area under the curve (AUC) of 0.835, with sensitivity of 80.1% and specificity of 68.4%. AI algorithms predicted the spontaneous stone passage up to 92% sensitivity and up to 86% specifity. CONCLUSIONS AI algorithms are high-powered alternatives that can be used in the prediction of spontaneous distal ureteral stone passage.
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Affiliation(s)
- Tugay Aksakalli
- Department of Urology, Erzurum Regional Training and Research Hospital, Erzurum, Turkey.
| | | | | | - Adem Utlu
- Department of Urology, Erzurum Regional Training and Research Hospital, Erzurum, Turkey
| | | | - Feyzullah Celik
- Department of Urology, Erzurum Regional Training and Research Hospital, Erzurum, Turkey
| | - Ibrahim Karabulut
- Department of Urology, Erzurum Regional Training and Research Hospital, Erzurum, Turkey
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Tano ZE, Cumpanas AD, Gorgen ARH, Rojhani A, Altamirano-Villarroel J, Landman J. Surgical Artificial Intelligence: Endourology. Urol Clin North Am 2024; 51:77-89. [PMID: 37945104 DOI: 10.1016/j.ucl.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Endourology is ripe with information that includes patient factors, laboratory tests, outcomes, and visual data, which is becoming increasingly complex to assess. Artificial intelligence (AI) has the potential to explore and define these relationships; however, humans might not be involved in the input, analysis, or even determining the methods of analysis. Herein, the authors present the current state of AI in endourology and highlight the need for urologists to share their proposed AI solutions for reproducibility outside of their institutions and prepare themselves to properly critique this new technology.
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Affiliation(s)
- Zachary E Tano
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA.
| | - Andrei D Cumpanas
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Antonio R H Gorgen
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Allen Rojhani
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Jaime Altamirano-Villarroel
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Jaime Landman
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
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Cao J, Inadomi MJ, Daignault-Newton S, Dauw CA, George A, Hiller S, Ghani KR, Krumm AE, Singh K. Development and Validation of a Model to Predict Ureteral Stent Placement Following Ureteroscopy: Results From a Statewide Collaborative. Urology 2023; 177:34-40. [PMID: 37044310 DOI: 10.1016/j.urology.2023.01.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 12/22/2022] [Accepted: 01/03/2023] [Indexed: 04/14/2023]
Abstract
OBJECTIVE To develop and validate a model to predict whether patients undergoing ureteroscopy (URS) will receive a stent. METHODS Using registry data obtained from the Michigan Urological Surgery Improvement Collaborative Reducing Operative Complications from Kidney Stones initiative, we identified patients undergoing URS from 2016 to 2020. We used patients' age, sex, body mass index, size and location of the largest stone, current stent in place, history of any kidney stone procedure, procedure type, and acuity to fit a multivariable logistic regression model to a derivation cohort consisting of a random two-thirds of episodes. Model discrimination and calibration were evaluated in the validation cohort. A sensitivity analysis examined urologist variation using generalized mixed-effect models. RESULTS We identified 15,048 URS procedures, of which 11,471 (76%) had ureteral stents placed. Older age, male sex, larger stone size, the largest stone being in the ureteropelvic junction, no prior stone surgery, no stent in place, a planned procedure type of laser lithotripsy, and urgent procedure were associated with a higher risk of stent placement. The model achieved an area under the receiver operating characteristic curve of 0.69 (95% CI 0.67, 0.71). Incorporating urologist-level variation improved the area under the receiver operating characteristic curve to 0.83 (95% CI 0.82, 0.84). CONCLUSION Using a large clinical registry, we developed a multivariable regression model to predict ureteral stent placement following URS. Though well-calibrated, the model had modest discrimination due to heterogeneity in practice patterns in stent placement across urologists.
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Affiliation(s)
- Jie Cao
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI
| | | | | | - Casey A Dauw
- Department of Urology, University of Michigan Medical School, Ann Arbor, MI
| | - Arvin George
- Department of Urology, University of Michigan Medical School, Ann Arbor, MI
| | - Spencer Hiller
- Department of Urology, University of Michigan Medical School, Ann Arbor, MI
| | - Khurshid R Ghani
- Department of Urology, University of Michigan Medical School, Ann Arbor, MI
| | - Andrew E Krumm
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI; University of Michigan School of Information, Ann Arbor, MI
| | - Karandeep Singh
- Department of Urology, University of Michigan Medical School, Ann Arbor, MI; Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI; University of Michigan School of Information, Ann Arbor, MI; Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI.
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4
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Anastasiadis A, Koudonas A, Langas G, Tsiakaras S, Memmos D, Mykoniatis I, Symeonidis EN, Tsiptsios D, Savvides E, Vakalopoulos I, Dimitriadis G, de la Rosette J. Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review. Asian J Urol 2023; 10:258-274. [PMID: 37538159 PMCID: PMC10394286 DOI: 10.1016/j.ajur.2023.02.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/23/2022] [Accepted: 02/10/2023] [Indexed: 08/05/2023] Open
Abstract
Objective To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease. Methods A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ''endourology'', ''artificial intelligence'', ''machine learning'', and ''urolithiasis'' were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language. Results A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors. Conclusion AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.
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Affiliation(s)
- Anastasios Anastasiadis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Antonios Koudonas
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Georgios Langas
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Stavros Tsiakaras
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Dimitrios Memmos
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Ioannis Mykoniatis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Evangelos N. Symeonidis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Dimitrios Tsiptsios
- Neurology Department, Democritus University of Thrace, Alexandroupolis, Greece
| | | | - Ioannis Vakalopoulos
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Georgios Dimitriadis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Jean de la Rosette
- Department of Urology, Istanbul Medipol Mega University Hospital, Istanbul, Turkey
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Bouhadana D, Lu XH, Luo JW, Assad A, Deyirmendjian C, Guennoun A, Nguyen DD, Kwong JCC, Chughtai B, Elterman D, Zorn KC, Trinh QD, Bhojani N. Clinical Applications of Machine Learning for Urolithiasis and Benign Prostatic Hyperplasia: A Systematic Review. J Endourol 2022; 37:474-494. [PMID: 36266993 DOI: 10.1089/end.2022.0311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION Previous systematic reviews related to machine learning (ML) in urology often overlooked the literature related to endourology. Therefore, we aim to conduct a more focused systematic review examining the use of ML algorithms for benign prostatic hyperplasia (BPH) or urolithiasis. In addition, we are the first group to evaluate these articles using the STREAM-URO framework. METHODS Searches of MEDLINE, Embase, and the Cochrane CENTRAL databases were conducted from inception through July 12, 2021. Keywords included those related to ML, endourology, urolithiasis, and BPH. Two reviewers screened the citations that were eligible for title, abstract and full-text screening, with conflicts resolved by a third reviewer. Two reviewers extracted information from the studies, with discrepancies resolved by a third reviewer. The data collected was then qualitatively synthesized by consensus. Two reviewers evaluated each article according to the STREAM-URO checklist with discrepancies resolved by a third reviewer. RESULTS After identifying 459 unique citations, 63 articles were retained for data extraction. Most articles consisted of tabular (n=32) and computer vision (n=23) tasks. The two most common problem types were classification (n=40) and regression (n=12). In general, most studies utilized neural networks as their ML algorithm (n=36). Among the 63 studies retrieved, 58 were related to urolithiasis and five focused on BPH. The urolithiasis studies were designed for outcome prediction (n=20), stone classification (n=18), diagnostics (n=17), and therapeutics (n=3). The BPH studies were designed for outcome prediction (n=2), diagnostics (n=2), and therapeutics (n=1). On average, the urolithiasis and BPH articles met 13.8 (SD 2.6), and 13.4 (4.1) of the 26 STREAM-URO framework criteria, respectively. CONCLUSIONS The majority of the retrieved studies successfully helped with outcome prediction, diagnostics, and therapeutics for both urolithiasis and BPH. While ML shows great promise in improving patient care, it is important to adhere to the recently developed STREAM-URO framework to ensure the development of high-quality ML studies.
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Affiliation(s)
- David Bouhadana
- McGill University Faculty of Medicine and Health Sciences, 12367, 3605 de la Montagne, Montreal, Quebec, Canada, H3G 2M1;
| | - Xing Han Lu
- McGill University School of Computer Science, 348406, Montreal, Quebec, Canada;
| | - Jack W Luo
- McGill University Faculty of Medicine and Health Sciences, 12367, Montreal, Quebec, Canada;
| | - Anis Assad
- University of Montreal Hospital Centre, 25443, Urology, Montreal, Quebec, Canada;
| | | | - Abbas Guennoun
- University of Montreal Hospital Centre, 25443, Urology, Montreal, Quebec, Canada;
| | | | | | - Bilal Chughtai
- Weill Cornell Medical Center, Urology, New York, New York, United States;
| | - Dean Elterman
- University of Toronto, 7938, Urology, Toronto, Ontario, Canada;
| | | | - Quoc-Dien Trinh
- Brigham and Women's Hospital, Urology, Boston, Massachusetts, United States;
| | - Naeem Bhojani
- University of Montreal Hospital Centre, 25443, Urology, Montreal, Quebec, Canada;
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The identification of pregnant women with renal colic who may need surgical intervention. BMC Urol 2022; 22:30. [PMID: 35255882 PMCID: PMC8903555 DOI: 10.1186/s12894-022-00985-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 02/25/2022] [Indexed: 11/18/2022] Open
Abstract
Background Renal colic is a surgical emergency in pregnancy that is caused by a range of non-obstetric factors and known to occur more frequently during the second and third trimesters. Several studies have reported that up to 70–80% of stones pass spontaneously during pregnancy. There are some patients will not pass their stones and will ultimately require surgical intervention. Through retrospective analysis of the clinical data of 212 pregnant women with renal colic, the predictive factors of pregnant women with renal colic in need of surgical intervention were determined. Methods We conducted a retrospective review of 212 pregnant women presenting with renal colic between 1st January 2009 and 31st December 2020. Univariate and multivariate analyses identified a range of predictive variables for surgical intervention. In addition, we used receiver operating characteristic curve analysis to evaluate the predictive power of our model and created a nomogram for clinical application. Results Of the 212 patients presenting with acute renal colic in pregnancy, 100 patients (47.2%) underwent surgical intervention and 112 patients (52.8%) were treated conservatively. Univariate analysis identified significant differences between the two groups with regards to fever, the duration of pain, white blood cells, C-reactive protein, ureteral stone size, hydronephrosis, and stone location. Multivariate analysis further identified a number of independent predictors for surgical intervention, including fever, a duration of pain ≥ 4 days, a ureteral stone size ≥ 8 mm, and moderate or severe hydronephrosis. Conclusions We identified several independent predictors for surgical intervention for renal colic in pregnancy. Fever, a duration of pain ≥ 4 days, a ureteral stone size ≥ 8 mm, and moderate/severe hydronephrosis, play significant roles in predicting surgical intervention. Our nomogram can help to calculate the probability of surgical intervention in a simple and efficient manner. Prospective studies are now required to validate our model.
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Abstract
PURPOSE OF REVIEW Artificial intelligence in medicine has allowed for efficient processing of large datasets to perform cognitive tasks that facilitate clinical decision-making, and it is an emerging area of research. This review aims to highlight the most pertinent and recent research in artificial intelligence in endourology, where it has been used to optimize stone diagnosis, support decision-making regarding management, predict stone recurrence, and provide new tools for bioinformatics research within endourology. RECENT FINDINGS Artificial neural networks (ANN) and machine learning approaches have demonstrated high accuracy in predicting stone diagnoses, stone composition, and outcomes of spontaneous stone passage, shockwave lithotripsy (SWL), or percutaneous nephrolithotomy (PCNL); some of these models outperform more traditional predictive models and existing nomograms. In addition, these approaches have been used to predict stone recurrence, quality of life scores, and provide novel methods of mining the electronic medical record for research. SUMMARY Artificial intelligence can be used to enhance existing approaches to stone diagnosis, management, and prevention to provide a more individualized approach to endourologic care. Moreover, it may support an emerging area of bioinformatics research within endourology. However, despite high accuracy, many of the published algorithms lack external validity and require further study before they are more widely adopted.
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Artificial Intelligence in Urology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Hameed BMZ, Shah M, Naik N, Rai BP, Karimi H, Rice P, Kronenberg P, Somani B. The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades. Curr Urol Rep 2021; 22:53. [PMID: 34626246 PMCID: PMC8502128 DOI: 10.1007/s11934-021-01069-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/27/2021] [Indexed: 12/17/2022]
Abstract
Purpose of Review To highlight and review the application of artificial intelligence (AI) in kidney stone disease (KSD) for diagnostics, predicting procedural outcomes, stone passage, and recurrence rates. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Recent Findings This review discusses the newer advancements in AI-driven management strategies, which holds great promise to provide an essential step for personalized patient care and improved decision making. AI has been used in all areas of KSD including diagnosis, for predicting treatment suitability and success, basic science, quality of life (QOL), and recurrence of stone disease. However, it is still a research-based tool and is not used universally in clinical practice. This could be due to a lack of data infrastructure needed to train the algorithms, wider applicability in all groups of patients, complexity of its use and cost involved with it. Summary The constantly evolving literature and future research should focus more on QOL and the cost of KSD treatment and develop evidence-based AI algorithms that can be used universally, to guide urologists in the management of stone disease.
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Affiliation(s)
- B M Zeeshan Hameed
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.,iTRUE: International Training and Research, Uro-Oncology and Endourology, Manipal, Karnataka, India
| | - Milap Shah
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.,iTRUE: International Training and Research, Uro-Oncology and Endourology, Manipal, Karnataka, India
| | - Nithesh Naik
- iTRUE: International Training and Research, Uro-Oncology and Endourology, Manipal, Karnataka, India. .,Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
| | - Bhavan Prasad Rai
- iTRUE: International Training and Research, Uro-Oncology and Endourology, Manipal, Karnataka, India.,Freeman Hospital, Newcastle upon Tyne, UK
| | - Hadis Karimi
- Department of Pharmacy, Manipal College of Pharmaceuticals, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Patrick Rice
- Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK
| | | | - Bhaskar Somani
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.,iTRUE: International Training and Research, Uro-Oncology and Endourology, Manipal, Karnataka, India.,Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK
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Salem H, Soria D, Lund JN, Awwad A. A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology. BMC Med Inform Decis Mak 2021; 21:223. [PMID: 34294092 PMCID: PMC8299670 DOI: 10.1186/s12911-021-01585-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 07/08/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Testing a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified. METHODS The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system. RESULTS The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible. CONCLUSION ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.
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Affiliation(s)
- Hesham Salem
- Urological Department, NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, NG72UH, UK
- University Hospitals of Derby and Burton NHS Foundation Trust, Royal Derby Hospital, University of Nottingham, Derby, DE22 3DT, UK
| | - Daniele Soria
- School of Computer Science and Engineering, University of Westminster, London, W1W 6UW, UK
| | - Jonathan N Lund
- University Hospitals of Derby and Burton NHS Foundation Trust, Royal Derby Hospital, University of Nottingham, Derby, DE22 3DT, UK
| | - Amir Awwad
- NIHR Nottingham Biomedical Research Centre, Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, NG72UH, UK.
- Department of Medical Imaging, London Health Sciences Centre, University of Hospital, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
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Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is the ability of a machine, or computer, to simulate intelligent behavior. In medicine, the use of large datasets enables a computer to learn how to perform cognitive tasks, thereby facilitating medical decision-making. This review aims to describe advancements in AI in stone disease to improve diagnostic accuracy in determining stone composition, to predict outcomes of surgical procedures or watchful waiting and ultimately to optimize treatment choices for patients. RECENT FINDINGS AI algorithms show high accuracy in different realms including stone detection and in the prediction of surgical outcomes. There are machine learning algorithms for outcomes after percutaneous nephrolithotomy, extracorporeal shockwave lithotripsy, and for ureteral stone passage. Some of these algorithms show better predictive capabilities compared to existing scoring systems and nomograms. SUMMARY The use of AI can facilitate the development of diagnostic and treatment algorithms in patients with stone disease. Although the generalizability and external validity of these algorithms remain uncertain, the development of highly accurate AI-based tools may enable the urologist to provide more customized patient care and superior outcomes.
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Affiliation(s)
| | - Margaret S Pearle
- Professor of Urology and Internal Medicine, Charles and Jane Pak Center for Mineral Metabolism, UT Southwestern Medical Center, Dallas, TX, USA
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12
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Mai RY, Zeng J, Meng WD, Lu HZ, Liang R, Lin Y, Wu GB, Li LQ, Ma L, Ye JZ, Bai T. Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion. BMC Cancer 2021; 21:283. [PMID: 33726693 PMCID: PMC7962237 DOI: 10.1186/s12885-021-07969-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/24/2021] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. This study aimed to develop and validate an artificial neural network (ANN) model to predict PHER in HCC patients without macroscopic vascular invasion. METHODS Nine hundred and three patients who underwent curative liver resection for HCC participated in this study. They were randomly divided into derivation (n = 679) and validation (n = 224) cohorts. The ANN model was developed in the derivation cohort and subsequently verified in the validation cohort. RESULTS PHER morbidity in the derivation and validation cohorts was 34.8 and 39.2%, respectively. A multivariable analysis revealed that hepatitis B virus deoxyribonucleic acid load, γ-glutamyl transpeptidase level, α-fetoprotein level, tumor size, tumor differentiation, microvascular invasion, satellite nodules, and blood loss were significantly associated with PHER. These factors were incorporated into an ANN model, which displayed greater discriminatory abilities than a Cox's proportional hazards model, preexisting recurrence models, and commonly used staging systems for predicting PHER. The recurrence-free survival curves were significantly different between patients that had been stratified into two risk groups. CONCLUSION When compared to other models and staging systems, the ANN model has a significant advantage in predicting PHER for HCC patients without macroscopic vascular invasion.
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Affiliation(s)
- Rong-Yun Mai
- Department of Hepatobilliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, 71 He Di Road, Nanning, China
- Department of Experimental Research, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China
| | - Jie Zeng
- Department of Experimental Research, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China
| | - Wei-da Meng
- Department of Hepatobilliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, 71 He Di Road, Nanning, China
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China
| | - Hua-Ze Lu
- Department of Hepatobilliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, 71 He Di Road, Nanning, China
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China
| | - Rong Liang
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China
- Department of First Chemotherapy, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Yan Lin
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China
- Department of First Chemotherapy, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Guo-Bin Wu
- Department of Hepatobilliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, 71 He Di Road, Nanning, China
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China
| | - Le-Qun Li
- Department of Hepatobilliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, 71 He Di Road, Nanning, China
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China
| | - Liang Ma
- Department of Hepatobilliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, 71 He Di Road, Nanning, China
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China
| | - Jia-Zhou Ye
- Department of Hepatobilliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, 71 He Di Road, Nanning, China.
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China.
| | - Tao Bai
- Department of Hepatobilliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, 71 He Di Road, Nanning, China.
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, 530021, China.
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Chu KY, Tradewell MB. Artificial Intelligence in Urology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_172-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Shah M, Naik N, Somani BK, Hameed BMZ. Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study. Turk J Urol 2020; 46:S27-S39. [PMID: 32479253 PMCID: PMC7731952 DOI: 10.5152/tud.2020.20117] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 04/12/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed. MATERIAL AND METHODS Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords "urology," "artificial intelligence," "machine learning," "deep learning," "artificial neural networks," "computer vision," and "natural language processing" were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. RESULTS The article reviewed 47 articles that reported characteristics and implementation of AI in urological cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively. CONCLUSIONS In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process.
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Affiliation(s)
- Milap Shah
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
| | - Nithesh Naik
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bhaskar K. Somani
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- Department of Urological Surgery, University Hospital Southampton NHS Trust, Southampton, UK
| | - BM Zeeshan Hameed
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- KMC Innovation Centre, Manipal Academy of Higher Education, Manipal, Karnataka, India
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Mai RY, Lu HZ, Bai T, Liang R, Lin Y, Ma L, Xiang BD, Wu GB, Li LQ, Ye JZ. Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma. Surgery 2020; 168:643-652. [DOI: 10.1016/j.surg.2020.06.031] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 06/07/2020] [Accepted: 06/23/2020] [Indexed: 02/06/2023]
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Predictors of surgical intervention following initial surveillance for acute ureteric colic. World J Urol 2018; 36:1477-1483. [DOI: 10.1007/s00345-018-2279-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 03/24/2018] [Indexed: 10/17/2022] Open
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Al-Harazi O, Al Insaif S, Al-Ajlan MA, Kaya N, Dzimiri N, Colak D. Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network. J Genet Genomics 2015; 43:349-67. [PMID: 27318646 DOI: 10.1016/j.jgg.2015.11.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Revised: 10/22/2015] [Accepted: 11/20/2015] [Indexed: 12/16/2022]
Abstract
A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.
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Affiliation(s)
- Olfat Al-Harazi
- Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Sadiq Al Insaif
- Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Monirah A Al-Ajlan
- Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Namik Kaya
- Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Nduna Dzimiri
- Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Dilek Colak
- Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.
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Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy. J Clin Neurosci 2015; 22:1444-9. [PMID: 26115898 DOI: 10.1016/j.jocn.2015.04.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 04/05/2015] [Indexed: 11/24/2022]
Abstract
This study introduces the use of multivariate linear regression (MLR) and support vector regression (SVR) models to predict postoperative outcomes in a cohort of patients who underwent surgery for cervical spondylotic myelopathy (CSM). Currently, predicting outcomes after surgery for CSM remains a challenge. We recruited patients who had a diagnosis of CSM and required decompressive surgery with or without fusion. Fine motor function was tested preoperatively and postoperatively with a handgrip-based tracking device that has been previously validated, yielding mean absolute accuracy (MAA) results for two tracking tasks (sinusoidal and step). All patients completed Oswestry disability index (ODI) and modified Japanese Orthopaedic Association questionnaires preoperatively and postoperatively. Preoperative data was utilized in MLR and SVR models to predict postoperative ODI. Predictions were compared to the actual ODI scores with the coefficient of determination (R(2)) and mean absolute difference (MAD). From this, 20 patients met the inclusion criteria and completed follow-up at least 3 months after surgery. With the MLR model, a combination of the preoperative ODI score, preoperative MAA (step function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.452; MAD=0.0887; p=1.17 × 10(-3)). With the SVR model, a combination of preoperative ODI score, preoperative MAA (sinusoidal function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.932; MAD=0.0283; p=5.73 × 10(-12)). The SVR model was more accurate than the MLR model. The SVR can be used preoperatively in risk/benefit analysis and the decision to operate.
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Huy NT, Thao NTH, Ha TTN, Lan NTP, Nga PTT, Thuy TT, Tuan HM, Nga CTP, Tuong VV, Dat TV, Huong VTQ, Karbwang J, Hirayama K. Development of clinical decision rules to predict recurrent shock in dengue. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2013; 17:R280. [PMID: 24295509 PMCID: PMC4057383 DOI: 10.1186/cc13135] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Accepted: 11/01/2013] [Indexed: 11/10/2022]
Abstract
INTRODUCTION Mortality from dengue infection is mostly due to shock. Among dengue patients with shock, approximately 30% have recurrent shock that requires a treatment change. Here, we report development of a clinical rule for use during a patient's first shock episode to predict a recurrent shock episode. METHODS The study was conducted in Center for Preventive Medicine in Vinh Long province and the Children's Hospital No. 2 in Ho Chi Minh City, Vietnam. We included 444 dengue patients with shock, 126 of whom had recurrent shock (28%). Univariate and multivariate analyses and a preprocessing method were used to evaluate and select 14 clinical and laboratory signs recorded at shock onset. Five variables (admission day, purpura/ecchymosis, ascites/pleural effusion, blood platelet count and pulse pressure) were finally trained and validated by a 10-fold validation strategy with 10 times of repetition, using a logistic regression model. RESULTS The results showed that shorter admission day (fewer days prior to admission), purpura/ecchymosis, ascites/pleural effusion, low platelet count and narrow pulse pressure were independently associated with recurrent shock. Our logistic prediction model was capable of predicting recurrent shock when compared to the null method (P < 0.05) and was not outperformed by other prediction models. Our final scoring rule provided relatively good accuracy (AUC, 0.73; sensitivity and specificity, 68%). Score points derived from the logistic prediction model revealed identical accuracy with AUCs at 0.73. Using a cutoff value greater than -154.5, our simple scoring rule showed a sensitivity of 68.3% and a specificity of 68.2%. CONCLUSIONS Our simple clinical rule is not to replace clinical judgment, but to help clinicians predict recurrent shock during a patient's first dengue shock episode.
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Multiple biomarker panels for early detection of breast cancer in peripheral blood. BIOMED RESEARCH INTERNATIONAL 2013; 2013:781618. [PMID: 24371830 PMCID: PMC3858861 DOI: 10.1155/2013/781618] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Accepted: 11/08/2013] [Indexed: 12/29/2022]
Abstract
Detecting breast cancer at early stages can be challenging. Traditional mammography and tissue microarray that have been studied for early breast cancer detection and prediction have many drawbacks. Therefore, there is a need for more reliable diagnostic tools for early detection of breast cancer due to a number of factors and challenges. In the paper, we presented a five-marker panel approach based on SVM for early detection of breast cancer in peripheral blood and show how to use SVM to model the classification and prediction problem of early detection of breast cancer in peripheral blood. We found that the five-marker panel can improve the prediction performance (area under curve) in the testing data set from 0.5826 to 0.7879. Further pathway analysis showed that the top four five-marker panels are associated with signaling, steroid hormones, metabolism, immune system, and hemostasis, which are consistent with previous findings. Our prediction model can serve as a general model for multibiomarker panel discovery in early detection of other cancers.
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Ibrahim AK, Mahmood IH, Mahmood NS. Efficacy and safety of tamsulosin vs. alfuzosin as medical expulsive therapy for ureteric stones. Arab J Urol 2013; 11:142-7. [PMID: 26558072 PMCID: PMC4443000 DOI: 10.1016/j.aju.2013.02.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2012] [Revised: 02/18/2013] [Accepted: 02/22/2013] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE To evaluate and compare the efficacy of tamsulosin and alfuzosin as medical expulsive therapy for ureteric stones. PATIENTS AND METHODS In all, 112 patients with ureteric stones of ⩽10 mm, located along the ureter, were randomly divided into three groups. In group I, 32 patients received no α-blockers (controls), in group II 40 patients received tamsulosin 0.4 mg daily, and in group III 40 patients received alfuzosin 10 mg daily. All patients were given analgesia and antibiotics when indicated. The follow-up was weekly for 4 weeks. RESULTS The mean stone size and age were comparable in the three groups. The stone expulsion rate was 44%, 85% and 75% in groups I, II and III, respectively. Half of the stones in group II passed within 2 weeks, half in group III passed within 3 weeks, while more than half of the stones in group I did not pass even after 4 weeks. The mean number of painful episodes was 2.45, 1.38 and 1.64 in groups I, II and III, respectively. The drug-related side-effects reported by patients were mild and transient. CONCLUSION The use of tamsulosin or alfuzosin as medical expulsive therapy for ureteric stones in the three sections of the ureter (upper, middle and lower) was safe and effective, as shown by the increased overall stone expulsion rate, reduced stone expulsion time and fewer pain episodes. Tamsulosin was associated with a greater rate of stone expulsion than was alfuzosin.
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Affiliation(s)
- Ahmed K. Ibrahim
- Urology Division, Department of Surgery, University of Mosul, Mosul, Iraq
| | - Isam H. Mahmood
- Department of Pharmacology, College of Pharmacy, University of Mosul, Mosul, Iraq
| | - Nada S. Mahmood
- Department of Pharmacology, College of Medicine, University of Mosul, Mosul, Iraq
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Seitz C. Medical Expulsive Therapy of Ureteral Calculi and Supportive Therapy After Extracorporeal Shock Wave Lithotripsy. ACTA ACUST UNITED AC 2010. [DOI: 10.1016/j.eursup.2010.11.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Brown AM, Schriger DL, Barrett TW. Outcome Measures, Interim Analyses, and Bayesian Approaches to Randomized Trials. Ann Emerg Med 2010; 55:216-224.e1. [DOI: 10.1016/j.annemergmed.2009.09.027] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Tonello L, Vescini F, Caudarella R. Support vector machines versus artificial neural network: Who is the winner? Kidney Int 2007; 71:84; author reply 84-5. [PMID: 17167512 DOI: 10.1038/sj.ki.5001907] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Abate A, Dal Moro F, Lanckriet G. Response to ‘Support vector machines versus artificial neural network: Who is the winner?’. Kidney Int 2007. [DOI: 10.1038/sj.ki.5001938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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