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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: 3] [Impact Index Per Article: 1.5] [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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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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Eun SJ, Kim J, Kim KH. Applications of artificial intelligence in urological setting: a hopeful path to improved care. J Exerc Rehabil 2021; 17:308-312. [PMID: 34805018 PMCID: PMC8566099 DOI: 10.12965/jer.2142596.298] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 10/10/2021] [Indexed: 11/22/2022] Open
Abstract
Artificial intelligence (AI) has been introduced in urology research and practice. Application of AI leads to better accuracy of disease diagnosis and predictive model for monitoring of responses to medical treatments. This mini-review article aims to summarize current applications and development of AI in urology setting, in particular for diagnosis and treatment of urological diseases. This review will introduce that machine learning algorithm-based models will enhance the prediction accuracy for various bladder diseases including interstitial cystitis, bladder cancer, and reproductive urology.
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Affiliation(s)
- Sung-Jong Eun
- Digital Health Industry Team, National IT Industry Promotion Agency, Jincheon, Korea
| | - Jayoung Kim
- Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Khae Hawn Kim
- Department of Urology, Chungnam National University Sejong Hospital, Chungnam National University School of Medicine, Sejong, Korea
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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: 14] [Impact Index Per Article: 4.7] [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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Hameed BMZ, Shah M, Naik N, Singh Khanuja H, Paul R, Somani BK. 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. J Endourol 2021; 35:1307-1313. [PMID: 33691473 DOI: 10.1089/end.2020.1136] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Objective: To develop a decision support system (DSS) for the prediction of the postoperative outcome of a kidney stone treatment procedure, particularly percutaneous nephrolithotomy (PCNL) to serve as a promising tool to provide counseling before an operation. Materials and Methods: The overall procedure includes data collection and prediction model development. Pre-/postoperative variables of 100 patients with staghorn calculus, who underwent PCNL, were collected. For feature vector, variables and categories including patient history variables, kidney stone parameters, and laboratory data were considered. The prediction model was developed using machine learning techniques, which include dimensionality reduction and supervised classification. Multiple classifier scheme was used for prediction. The derived DSS was evaluated by running the leave-one-patient-out cross-validation approach on the data set. Results: The system provided favorable accuracy (81%) in predicting the outcome of a treatment procedure. Performance in predicting the stone-free rate with the Minimum Redundancy Maximum Relevance feature (MRMR) treatment extracting top 3 features using Random Forest (RF) was 67%, with MRMR treatment extracting top 5 features using RF was 63%, and with MRMR treatment extracting top 10 features using Decision Tree was 62%. The statistical significance using standard error between the best area under the curves (AUCs) obtained from the Linear Discriminant Analysis (LDA) and MRMR. The results obtained from the LDA approach (0.81 AUC) was statistically significant (p = 0.027, z = 2.21) from the MRMR (0.64 AUC) (p = 0.05). Conclusion: The promising results of the developed DSS could be used in assisting urologists to provide counseling, predict a surgical outcome, and ultimately choose an appropriate surgical treatment for removing kidney stones.
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Affiliation(s)
- B M Zeeshan Hameed
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.,KMC Innovation Centre, Manipal Academy of Higher Education, Manipal, Karnataka, India.,iTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka
| | - Milap Shah
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.,iTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka
| | - Nithesh Naik
- iTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka.,Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Harneet Singh Khanuja
- Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Rahul Paul
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bhaskar K Somani
- iTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka.,Department of Urology, University Hospital Southampton NHS Trust, Southampton, United Kingdom
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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: 47] [Impact Index Per Article: 11.8] [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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Suarez-Ibarrola R, Hein S, Reis G, Gratzke C, Miernik A. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol 2019; 38:2329-2347. [PMID: 31691082 DOI: 10.1007/s00345-019-03000-5] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 10/25/2019] [Indexed: 01/15/2023] Open
Abstract
PURPOSE The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date. METHODS A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa). RESULTS In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods. CONCLUSIONS The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.
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Affiliation(s)
- Rodrigo Suarez-Ibarrola
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany.
| | - Simon Hein
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Gerd Reis
- Department Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
| | - Christian Gratzke
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Arkadiusz Miernik
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany
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