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Shah N, Khalid U, Kavia R, Batura D. Current advances in the use of artificial intelligence in predicting and managing urological complications. Int Urol Nephrol 2024:10.1007/s11255-024-04149-8. [PMID: 38982018 DOI: 10.1007/s11255-024-04149-8] [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: 04/30/2024] [Accepted: 07/03/2024] [Indexed: 07/11/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a promising avenue for improving patient care and surgical outcomes in urological surgery. However, the extent of AI's impact in predicting and managing complications is not fully elucidated. OBJECTIVES We review the application of AI to foresee and manage complications in urological surgery, assess its efficacy, and discuss challenges to its use. METHODS AND MATERIALS A targeted non-systematic literature search was conducted using the PubMed and Google Scholar databases to identify studies on AI in urological surgery and its complications. Evidence from the studies was synthesised. RESULTS Incorporating AI into various facets of urological surgery has shown promising advancements. From preoperative planning to intraoperative guidance, AI is revolutionising the field, demonstrating remarkable proficiency in tasks such as image analysis, decision-making support, and complication prediction. Studies show that AI programmes are highly accurate, increase surgical precision and efficiency, and reduce complications. However, implementation challenges exist in AI errors, human errors, and ethical issues. CONCLUSION AI has great potential in predicting and managing surgical complications of urological surgery. Advancements have been made, but challenges and ethical considerations must be addressed before widespread AI implementation.
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Affiliation(s)
- Nikhil Shah
- Faculty of Medicine, Medical University of Plovdiv, 4002, Plovdiv, Bulgaria
| | - Usman Khalid
- Faculty of Medicine, Medical University of Plovdiv, 4002, Plovdiv, Bulgaria
| | - Rajesh Kavia
- Department of Urology, London North West University Healthcare NHS Trust, Watford Road, Harrow, London, HA1 3UJ, UK
| | - Deepak Batura
- Department of Urology, London North West University Healthcare NHS Trust, Watford Road, Harrow, London, HA1 3UJ, UK.
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Khondker A, Kwong JCC, Rickard M, Erdman L, Kim JK, Ahmad I, Weaver J, Fernandez N, Tasian GE, Kulkarni GS, Lorenzo AJ. Application of STREAM-URO and APPRAISE-AI reporting standards for artificial intelligence studies in pediatric urology: A case example with pediatric hydronephrosis. J Pediatr Urol 2024; 20:455-467. [PMID: 38331659 DOI: 10.1016/j.jpurol.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/20/2023] [Accepted: 01/20/2024] [Indexed: 02/10/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) and machine learning (ML) in pediatric urology is gaining increased popularity and credibility. However, the literature lacks standardization in reporting and there are areas for methodological improvement, which incurs difficulty in comparison between studies and may ultimately hurt clinical implementation of these models. The "STandardized REporting of Applications of Machine learning in UROlogy" (STREAM-URO) framework provides methodological instructions to improve transparent reporting in urology and APPRAISE-AI in a critical appraisal tool which provides quantitative measures for the quality of AI studies. The adoption of these will allow urologists and developers to ensure consistency in reporting, improve comparison, develop better models, and hopefully inspire clinical translation. METHODS In this article, we have applied STREAM-URO framework and APPRAISE-AI tool to the pediatric hydronephrosis literature. By doing this, we aim to describe best practices on ML reporting in urology with STREAM-URO and provide readers with a critical appraisal tool for ML quality with APPRAISE-AI. By applying these to the pediatric hydronephrosis literature, we provide some tutorial for other readers to employ these in developing and appraising ML models. We also present itemized recommendations for adequate reporting, and critically appraise the quality of ML in pediatric hydronephrosis insofar. We provide examples of strong reporting and highlight areas for improvement. RESULTS There were 8 ML models applied to pediatric hydronephrosis. The 26-item STREAM-URO framework is provided in Appendix A and 24-item APPRAISE-AI tool is provided in Appendix B. Across the 8 studies, the median compliance with STREAM-URO was 67 % and overall study quality was moderate. The highest scoring APPRAISE-AI domains in pediatric hydronephrosis were clinical relevance and reporting quality, while the worst were methodological conduct, robustness of results, and reproducibility. CONCLUSIONS If properly conducted and reported, ML has the potential to impact the care we provide to patients in pediatric urology. While AI is exciting, the paucity of strong evidence limits our ability to translate models to practice. The first step toward this goal is adequate reporting and ensuring high quality models, and STREAM-URO and APPRAISE-AI can facilitate better reporting and critical appraisal, respectively.
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Affiliation(s)
- Adree Khondker
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada; Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Temerty Center for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Jethro C C Kwong
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Temerty Center for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Mandy Rickard
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - Lauren Erdman
- Temerty Center for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada; Center for Computational Medicine, Hospital for Sick Children, Toronto, ON, Canada; Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Jin K Kim
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada; Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Ihtisham Ahmad
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - John Weaver
- Division of Urology, Cleveland Clinic Children's, Cleveland, OH, USA
| | - Nicolas Fernandez
- Division of Urology, Seattle Children's Hospital, University of Washington, Seattle, WA, USA
| | - Gregory E Tasian
- Division of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Girish S Kulkarni
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Princess Margaret Cancer Center, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Armando J Lorenzo
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada; Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
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Ma W, Gao H, Chang M, Lu Z, Li D, Ding C, Bi D, Sun F. The construction of a nomogram to predict the prognosis and recurrence risks of UPJO. Front Pediatr 2024; 12:1376196. [PMID: 38633323 PMCID: PMC11022601 DOI: 10.3389/fped.2024.1376196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/13/2024] [Indexed: 04/19/2024] Open
Abstract
Objective This study was conducted to explore the risk factors for the prognosis and recurrence of ureteropelvic junction obstruction (UPJO). Methods The correlation of these variables with the prognosis and recurrence risks was analyzed by binary and multivariate logistic regression. Besides, a nomogram was constructed based on the multivariate logistic regression calculation. After the model was verified by the C-statistic, the ROC curve was plotted to evaluate the sensitivity of the model. Finally, the decision curve analysis (DCA) was conducted to estimate the clinical benefits and losses of intervention measures under a series of risk thresholds. Results Preoperative automated peritoneal dialysis (APD), preoperative urinary tract infection (UTI), preoperative renal parenchymal thickness (RPT), Mayo adhesive probability (MAP) score, and surgeon proficiency were the high-risk factors for the prognosis and recurrence of UPJO. In addition, a nomogram was constructed based on the above 5 variables. The area under the curve (AUC) was 0.8831 after self cross-validation, which validated that the specificity of the model was favorable. Conclusion The column chart constructed by five factors has good predictive ability for the prognosis and recurrence of UPJO, which may provide more reasonable guidance for the clinical diagnosis and treatment of this disease.
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Affiliation(s)
- Wenyue Ma
- Department of Pediatric Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Hongjie Gao
- Department of Pediatrics, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Mengmeng Chang
- Department of Pediatric Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Zhiyi Lu
- Department of Pediatric Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Ding Li
- Department of Pediatric Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Chen Ding
- Department of Pediatric Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Dan Bi
- Department of Pediatrics, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Fengyin Sun
- Department of Pediatric Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
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Hashemi Gheinani A, Kim J, You S, Adam RM. Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 2024; 21:214-242. [PMID: 37604982 DOI: 10.1038/s41585-023-00805-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 08/23/2023]
Abstract
The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
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Affiliation(s)
- Ali Hashemi Gheinani
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Urology, Inselspital, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Jina Kim
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rosalyn M Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Abstract
Application of artificial intelligence (AI) is one of the hottest topics in medicine. Unlike traditional methods that rely heavily on statistical assumptions, machine learning algorithms can identify highly complex patterns from data, allowing robust predictions. There is an abundance of evidence of exponentially increasing pediatric urologic publications using AI methodology in recent years. While these studies show great promise for better understanding of disease and patient care, we should be realistic about the challenges arising from the nature of pediatric urologic conditions and practice, in order to continue to produce high-impact research.
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Affiliation(s)
- Hsin-Hsiao Scott Wang
- Computational Healthcare Analytics Program, Department of Urology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, USA.
| | - Ranveer Vasdev
- Department of Urology, Mayo Clinic Rochester, 200 1st Street Southwest, Rochester, MN 55905, USA
| | - Caleb P Nelson
- Clinical and Health Services Research, Department of Urology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, USA
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Khondker A, Kwong JCC, Chancy M, D'Souza N, Kim K, Kim JK, Tse LN, Chua M, Yadav P, Erdman L, Weaver J, Lorenzo AJ, Rickard M. Predicting obstruction risk using common ultrasonography parameters in paediatric hydronephrosis with machine learning. BJU Int 2024; 133:79-86. [PMID: 37594786 DOI: 10.1111/bju.16159] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
OBJECTIVE To sensitively predict the risk of renal obstruction on diuretic renography using routine reported ultrasonography (US) findings, coupled with machine learning approaches, and determine safe criteria for deferral of diuretic renography. PATIENTS AND METHODS Patients from two institutions with isolated hydronephrosis who underwent a diuretic renogram within 3 months following renal US were included. Age, sex, and routinely reported US findings (laterality, kidney length, anteroposterior diameter, Society for Fetal Urology [SFU] grade) were abstracted. The drainage half-times were collected from renography and stratified as low risk (<20 min, primary outcome), intermediate risk (20-60 min), and high risk of obstruction (>60 min). A random Forest model was trained to classify obstruction risk, here named the 'Artificial intelligence Evaluation of Renogram Obstruction' (AERO). Model performance was determined by measuring area under the receiver-operating-characteristic curve (AUROC) and decision curve analysis. RESULTS A total of 304 patients met the inclusion criteria, with a median (interquartile range) age of diuretic renogram at 4 (2-7) months. Of all patients, 48 (16%) were low risk, 102 (33%) were intermediate risk, 156 (51%) were high risk of obstruction based on diuretic renogram. The AERO achieved a binary AUROC of 0.84, multi-class AUROC of 0.74 that was superior to the SFU grade, and external validation (n = 64) binary AUROC of 0.76. The most important features for prediction included age, anteroposterior diameter, and SFU grade. We deployed our application in an easy-to-use application (https://sickkidsurology.shinyapps.io/AERO/). At a threshold probability of 30%, the AERO would allow 66 more patients per 1000 to safely avoid a renogram without missing significant obstruction compared to a strategy in which a renogram is routinely performed for SFU Grade ≥3. CONCLUSIONS Coupled with machine learning, routine US findings can improve the criteria to determine in which children with isolated hydronephrosis a diuretic renogram can be safely avoided. Further optimisation and validation are required prior to implementation into clinical practice.
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Affiliation(s)
- Adree Khondker
- Temerty Faculty of Medicine, University of Toronto, Toronto, Onterio, Canada
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Onterio, Canada
| | - Jethro C C Kwong
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Onterio, Canada
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Onterio, Canada
| | - Margarita Chancy
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Onterio, Canada
| | - Neeta D'Souza
- Department of Urology, Rainbow Babies and Children's Hospital, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Division of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kellie Kim
- Temerty Faculty of Medicine, University of Toronto, Toronto, Onterio, Canada
| | - Jin K Kim
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Onterio, Canada
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Onterio, Canada
| | - Lai Nam Tse
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Onterio, Canada
| | - Michael Chua
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Onterio, Canada
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Onterio, Canada
| | - Priyank Yadav
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Onterio, Canada
| | - Lauren Erdman
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Onterio, Canada
- Vector Institute, Toronto, Onterio, Canada
| | - John Weaver
- Department of Urology, Rainbow Babies and Children's Hospital, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Division of Urology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Armando J Lorenzo
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Onterio, Canada
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Onterio, Canada
| | - Mandy Rickard
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Onterio, Canada
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Richter J, Rickard M, Kim JK, Erdman L, Lorenzo AJ, Chua M. Predicting the Future of Patients with Obstructive Uropathy—A Comprehensive Review. CURRENT PEDIATRICS REPORTS 2022. [DOI: 10.1007/s40124-022-00272-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Kim JK, Chua ME, Rickard M, Milford K, Keefe DT, Lorenzo AJ. Attaining competency and proficiency in open pyeloplasty: a learning curve configuration using cumulative sum analysis. Int Urol Nephrol 2022; 54:1857-1863. [PMID: 35588341 DOI: 10.1007/s11255-022-03229-x] [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: 02/28/2022] [Accepted: 04/25/2022] [Indexed: 10/18/2022]
Abstract
INTRODUCTION The learning curves for minimally invasive pyeloplasty techniques have been described in the past. However, the learning curve in achieving competency in open pyeloplasty has not been described. Hence, we aim to evaluate a single surgeon series of open pyeloplasty technique using the cumulative sum (CUSUM) methodology. METHODS We retrospectively reviewed all open pyeloplasties performed by a single surgeon (AJL) between January 2008 and March 2020. Collected variables included: sex, age at surgery, operative time, hospital stay, pre-operative ultrasound, pre-operative nuclear scans, pre-operative anteroposterior diameter, associated anomalies, laterality (left or right), type of stent, pre-operative split renal function, and duration of follow-up. A CUSUM analysis was used: the highest peak, plateau and downward trends for complications (defined as Clavien-Dindo classification ≥ 3b) were identified on the plot and set as the transition points between five phases (learning, competency, proficiency, case-mix, and mastery). RESULTS Based on the CUSUM analysis, the index surgeon reached the competency phase after performing their 13th open pyeloplasty and became proficient after the 70th case. In the case-mix phase (104th-126th cases), where the surgeon may be performing more complex cases while increasing trainee involvement, there was a slight increase in complication rates. After the 126th case, the surgeon entered the mastery phase, where there was consistent decreasing trend in complications. CONCLUSIONS Surgeons performing open pyeloplasty in children following completion of their surgical training will continue to learn through their early cases until achieving competency. Technical competency may be reached after the 13th case. In this report, we looked at the number of cases to become proficient in open pyeloplasty procedure in children. A surgeon may achieve technical proficiency in the procedure after their 13th case.
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Affiliation(s)
- Jin K Kim
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada. .,Division of Urology, Department of Surgery, The Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada.
| | - Michael E Chua
- Division of Urology, Department of Surgery, The Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada.,Institute of Urology, St. Luke's Medical Center, Quezon City, Philippines
| | - Mandy Rickard
- Division of Urology, Department of Surgery, The Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada
| | - Karen Milford
- Division of Urology, Department of Surgery, The Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada
| | - Daniel T Keefe
- Division of Urology, Department of Surgery, The Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada
| | - Armando J Lorenzo
- Division of Urology, Department of Surgery, The Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada
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