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Liu X, Zhong P, Gao Y, Liao L. Applications of machine learning in urodynamics: A narrative review. Neurourol Urodyn 2024; 43:1617-1625. [PMID: 38837301 DOI: 10.1002/nau.25490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 03/30/2024] [Accepted: 05/02/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND Machine learning algorithms as a research tool, including traditional machine learning and deep learning, are increasingly applied to the field of urodynamics. However, no studies have evaluated how to select appropriate algorithm models for different urodynamic research tasks. METHODS We undertook a narrative review evaluating how the published literature reports the applications of machine learning in urodynamics. We searched PubMed up to December 2023, limited to the English language. We selected the following search terms: artificial intelligence, machine learning, deep learning, urodynamics, and lower urinary tract symptoms. We identified three domains for assessment in advance of commencing the review. These were the applications of urodynamic studies examination, applications of diagnoses of dysfunction related to urodynamics, and applications of prognosis prediction. RESULTS The machine learning algorithm applied in the field of urodynamics can be mainly divided into three aspects, which are urodynamic examination, diagnosis of urinary tract dysfunction and prediction of the efficacy of various treatment methods. Most of these studies were single-center retrospective studies, lacking external validation, requiring further validation of model generalization ability, and insufficient sample size. The relevant research in this field is still in the preliminary exploration stage; there are few high-quality multi-center clinical studies, and the performance of various models still needs to be further optimized, and there is still a distance from clinical application. CONCLUSIONS At present, there is no research to summarize and analyze the machine learning algorithms applied in the field of urodynamics. The purpose of this review is to summarize and classify the machine learning algorithms applied in this field and to guide researchers to select the appropriate algorithm model for different task requirements to achieve the best results.
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Affiliation(s)
- Xin Liu
- School of Rehabilitation, Capital Medical University, Beijing, China
- Department of Urology, China Rehabilitation Research Centre, Beijing, China
| | - Ping Zhong
- Department of Urology, China Rehabilitation Research Centre, Beijing, China
| | - Yi Gao
- School of Rehabilitation, Capital Medical University, Beijing, China
- Department of Urology, China Rehabilitation Research Centre, Beijing, China
| | - Limin Liao
- School of Rehabilitation, Capital Medical University, Beijing, China
- Department of Urology, China Rehabilitation Research Centre, Beijing, China
- China Rehabilitation Science Institute, Beijing, China
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Gammie A, Arlandis S, Couri BM, Drinnan M, Carolina Ochoa D, Rantell A, de Rijk M, van Steenbergen T, Damaser M. Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI-RS 2023. Neurourol Urodyn 2024; 43:1337-1343. [PMID: 37921238 DOI: 10.1002/nau.25319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 10/22/2023] [Indexed: 11/04/2023]
Abstract
INTRODUCTION A "Think Tank" at the International Consultation on Incontinence-Research Society meeting held in Bristol, United Kingdom in June 2023 considered the progress and promise of machine learning (ML) applied to urodynamic data. METHODS Examples of the use of ML applied to data from uroflowmetry, pressure flow studies and imaging were presented. The advantages and limitations of ML were considered. Recommendations made during the subsequent debate for research studies were recorded. RESULTS ML analysis holds great promise for the kind of data generated in urodynamic studies. To date, ML techniques have not yet achieved sufficient accuracy for routine diagnostic application. Potential approaches that can improve the use of ML were agreed and research questions were proposed. CONCLUSIONS ML is well suited to the analysis of urodynamic data, but results to date have not achieved clinical utility. It is considered likely that further research can improve the analysis of the large, multifactorial data sets generated by urodynamic clinics, and improve to some extent data pattern recognition that is currently subject to observer error and artefactual noise.
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Affiliation(s)
- Andrew Gammie
- Bristol Urological Institute, Southmead Hospital, Bristol, UK
| | - Salvador Arlandis
- Urology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Bruna M Couri
- Laborie Medical Technologies, Portsmouth, New Hampshire, USA
| | - Michael Drinnan
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | | | - Angie Rantell
- Urogynaecology Department, King's College Hospital, London, UK
| | - Mathijs de Rijk
- Department of Urology, Maastricht University, Maastricht, The Netherlands
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3
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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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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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Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med 2024; 13:1061. [PMID: 38398374 PMCID: PMC10889757 DOI: 10.3390/jcm13041061] [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: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.
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Affiliation(s)
- Marta Brandão
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
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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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7
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Ding Z, Zhang W, Wang H, Ke H, Su D, Wang Q, Bian K, Su F, Xu K. An automatic diagnostic system for the urodynamic study applying in lower urinary tract dysfunction. Int Urol Nephrol 2024; 56:441-449. [PMID: 37755608 DOI: 10.1007/s11255-023-03795-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/18/2023] [Indexed: 09/28/2023]
Abstract
OBJECTIVE To establish an automatic diagnostic system based on machine learning for preliminarily analysis of urodynamic study applying in lower urinary tract dysfunction (LUTD). METHODS The eight most common conditions of LUTDs were included in the present study. A total of 527 eligible patients with complete data, from the year of 2015 to 2020, were enrolled in this study. In total, two global parameters (patients' age and sex) and 13 urodynamic parameters were considered to be the input for machine learning algorithms. Three machine learning approaches were applied and evaluated in this study, including Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM). RESULTS By applying machine learning algorithms into the 8 common LUTDs, the DT models achieved the AUC of 0.63-0.98, the LR models achieved the AUC of 0.73-0.99, and the SVM models achieved the AUC of 0.64-1.00. For mutually exclusive diagnoses of underactive detrusor and acontractile detrusor, we developed a classification model that classifies the patients into either of these two diseases or double-negative class. For this classification method, the DT models achieved the AUC of 0.82-0.85 and the SVM models achieved the AUC of 0.86-0.90. Among all these models, the LR and the SVM models showed better performance. The best model of these diagnostic tasks achieved an average AUC of 0.90 (0.90 ± 0.08). CONCLUSIONS An automatic diagnostic system was developed using three machine learning models in urodynamic studies. This automated machine learning process could lead to promising assistance and enhancements of diagnosis and provide more useful reference for LUTD treatment.
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Affiliation(s)
- Zehua Ding
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Weiyu Zhang
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Huanrui Wang
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Hanwei Ke
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Dongyu Su
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Qi Wang
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Kaigui Bian
- School of Computer Science, Peking University, Beijing, China
| | - Feng Su
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Kexin Xu
- Department of Urology, Peking University People's Hospital, Beijing, China.
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8
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Yuan G, Ge Z, Zheng J, Yan X, Fu M, Li M, Yang X, Tang L. CNN-based diagnosis model of children's bladder compliance using a single intravesical pressure signal. Comput Methods Biomech Biomed Engin 2024:1-12. [PMID: 38193146 DOI: 10.1080/10255842.2023.2301414] [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: 07/12/2023] [Accepted: 12/20/2023] [Indexed: 01/10/2024]
Abstract
Bladder compliance assessment is crucial for diagnosing bladder functional disorders, with urodynamic study (UDS) being the principal evaluation method. However, the application of UDS is intricate and time-consuming in children. So it'S necessary to develop an efficient bladder compliance screen approach before UDS. In this study, We constructed a dataset based on UDS and designed a 1D-CNN model to optimize and train the network. Then applied the trained model to a dataset obtained solely through a proposed perfusion experiment. Our model outperformed other algorithms. The results demonstrate the potential of our model to alert abnormal bladder compliance accurately and efficiently.
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Affiliation(s)
- Gang Yuan
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Zicong Ge
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jian Zheng
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiangming Yan
- Department of Surgery, Children's Hospital of Soochow University, Soochow University, Suzhou, China
| | - Mingcui Fu
- Department of Surgery, Children's Hospital of Soochow University, Soochow University, Suzhou, China
| | - Ming Li
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiaodong Yang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Liangfeng Tang
- Department of Pediatric Urology, Children's Hospital, Fudan University, Shanghai, China
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Botter SM, Kessler TM. Neuro-Urology and Biobanking: An Integrated Approach for Advancing Research and Improving Patient Care. Int J Mol Sci 2023; 24:14281. [PMID: 37762582 PMCID: PMC10531693 DOI: 10.3390/ijms241814281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/12/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Understanding the molecular mechanisms underlying neuro-urological disorders is crucial for the development of targeted therapeutic interventions. Through the establishment of comprehensive biobanks, researchers can collect and store various biological specimens, including urine, blood, tissue, and DNA samples, to study these mechanisms. In the context of neuro-urology, biobanking facilitates the identification of genetic variations, epigenetic modifications, and gene expression patterns associated with neurogenic lower urinary tract dysfunction. These conditions often present as symptoms of neurological diseases such as Alzheimer's disease, multiple sclerosis, Parkinson's disease, spinal cord injury, and many others. Biobanking of tissue specimens from such patients is essential to understand why these diseases cause the respective symptoms and what can be done to alleviate them. The utilization of high-throughput technologies, such as next-generation sequencing and gene expression profiling, enables researchers to explore the molecular landscape of these conditions in an unprecedented manner. The development of specific and reliable biomarkers resulting from these efforts may help in early detection, accurate diagnosis, and effective monitoring of neuro-urological conditions, leading to improved patient care and management. Furthermore, these biomarkers could potentially facilitate the monitoring of novel therapies currently under investigation in neuro-urological clinical trials. This comprehensive review explores the synergistic integration of neuro-urology and biobanking, with particular emphasis on the translation of biobanking approaches in molecular research in neuro-urology. We discuss the advantages of biobanking in neuro-urological studies, the types of specimens collected and their applications in translational research. Furthermore, we highlight the importance of standardization and quality assurance when collecting samples and discuss challenges that may compromise sample quality and impose limitations on their subsequent utilization. Finally, we give recommendations for sampling in multicenter studies, examine sustainability issues associated with biobanking, and provide future directions for this dynamic field.
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Affiliation(s)
- Sander M. Botter
- Swiss Center for Musculoskeletal Biobanking, Balgrist Campus AG, 8008 Zürich, Switzerland
| | - Thomas M. Kessler
- Department of Neuro-Urology, Balgrist University Hospital, University of Zürich, 8008 Zürich, Switzerland;
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10
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Zhou Q, Chen Z, Wu B, Lin D, Hu Y, Zhang X, Liu J. A Pilot Study: Detrusor Overactivity Diagnosis Method Based on Deep Learning. Urology 2023; 179:188-195. [PMID: 37315592 DOI: 10.1016/j.urology.2023.04.030] [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: 02/18/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVE To develop two intelligent diagnosis models of detrusor overactivity (DO) based on deep learning to assist doctors no longer heavily rely on visual observation of urodynamic study (UDS) curves. METHODS UDS curves of 92 patients were collected during 2019. We constructed two DO event recognition models based on convolutional neural network (CNN) with 44 samples, and tested the model performance with the remaining 48 samples by comparing other four classical machine learning models. During the testing phase, we developed a threshold screening strategy to quickly filter out suspected DO event segments in each patient's UDS curve. If two or more DO event fragments are determined to be DO by the diagnostic model, the patient is diagnosed as having DO. RESULTS We extracted 146 DO event samples and 1863 non-DO event samples from the UDS curves of 44 patients to train CNN models. Through 10-fold cross-validation, the training accuracy and validation accuracy of our models achieved the highest accuracy. In the model testing phase, we used a threshold screening strategy to quickly screen out the suspected DO event samples in the UDS curve of another 48 patients, and then input them into the trained models. Finally, the diagnostic accuracy of patients without DO and patients with DO was 78.12% and 100%, respectively. CONCLUSION Under the available data, the accuracy of the DO diagnostic model based on CNN is satisfactory. With the increase of the amount of data, the deep learning model is likely to have better performance. CLINICAL TRIAL REGISTRATION This experiment was certified by the Chinese Clinical Trial Registry (ChiCTR2200063467).
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Affiliation(s)
- Quan Zhou
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Zhong Chen
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Urology, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Bo Wu
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Dongxu Lin
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Youmin Hu
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
| | - Xin Zhang
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Jie Liu
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China
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11
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Ravishankar B, Vasdev RMS, Timm GW, Elliott S, Nakib NA, Johnson M, Nelson DE. Objective Quantification of Detrusor Overactivity Using Spectral Measures of Cystometry Data. Urology 2023; 174:206-211. [PMID: 36708933 DOI: 10.1016/j.urology.2023.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 01/11/2023] [Indexed: 01/26/2023]
Abstract
OBJECTIVE To develop scalable objective methods for differentiating patients with and without detrusor overactivity (DO) using quantitative Fast Fourier Transform (FFT)-based measures and routinely captured cystometry data. METHODS Retrospective cystometry data were collected as prevoid vesical and abdominal pressure signals from 18 DO and 10 SUI (non-DO) cystometry recordings. Data were filtered and divided into two equal-duration segments, Early and Late Fill, representing the first and second halves of filling. FFT was applied, followed by subtraction of abdominal spectra from vesical spectra. Spectral Power (SP) and Weighted Average Frequency (WAF) measures were calculated for each segment spectra within 1-6 cycles min-1. RESULTS Compared to non-DO, the mean SP was significantly higher in DO patients for both Early and Late Fill segments. WAF was significantly lower in DO patients for both segments. Changes in spectral pressures appeared to be linked to the presence of detrusor contractions (DCs) and were especially visible when DCs were present in the Early Fill segments of cystometry. CONCLUSION FFT-based spectral measures derived from routinely captured cystometry data are significantly different between DO and non-DO patients. This preliminary method is clinically scalable and can be further developed to facilitate the detection of DO, classify disease phenotype, and capture therapeutic efficacy.
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Affiliation(s)
- Bhaskar Ravishankar
- Department of Electrical Engineering, University of Minnesota Twin Cities, Minneapolis, MN; Department of Urology, University of Minnesota Twin Cities, Minneapolis, MN
| | - Ranveer M S Vasdev
- Department of Urology, University of Minnesota Twin Cities, Minneapolis, MN; Department of Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, MN
| | - Gerald W Timm
- Department of Electrical Engineering, University of Minnesota Twin Cities, Minneapolis, MN; Department of Urology, University of Minnesota Twin Cities, Minneapolis, MN
| | - Sean Elliott
- Department of Urology, University of Minnesota Twin Cities, Minneapolis, MN
| | - Nissrine A Nakib
- Department of Urology, University of Minnesota Twin Cities, Minneapolis, MN
| | - Matthew Johnson
- Department of Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, MN
| | - Dwight E Nelson
- Department of Urology, University of Minnesota Twin Cities, Minneapolis, MN.
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12
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Seval MM, Varlı B. Current developments in artificial intelligence from obstetrics and gynecology to urogynecology. Front Med (Lausanne) 2023; 10:1098205. [PMID: 36910480 PMCID: PMC9995368 DOI: 10.3389/fmed.2023.1098205] [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: 11/14/2022] [Accepted: 02/09/2023] [Indexed: 02/25/2023] Open
Abstract
In today's medical practice clinicians need to struggle with a huge amount of data to improve the outcomes of the patients. Sometimes one clinician needs to deal with thousands of ultrasound images or hundred papers of laboratory results. To overcome this shortage, computers get in help of human beings and they are educated under the term "artificial intelligence." We were using artificial intelligence in our daily lives (i.e., Google, Netflix, etc.), but applications in medicine are relatively new. In obstetrics and gynecology, artificial intelligence models mostly use ultrasound images for diagnostic purposes but nowadays researchers started to use other medical recordings like non-stress tests or urodynamics study results to develop artificial intelligence applications. Urogynecology is a developing subspecialty of obstetrics and gynecology, and articles about artificial intelligence in urogynecology are limited but in this review, we aimed to increase clinicians' knowledge about this new approach.
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Affiliation(s)
- Mehmet Murat Seval
- Department of Obstetrics and Gynecology, Ankara University School of Medicine, Ankara, Türkiye
| | - Bulut Varlı
- Department of Obstetrics and Gynecology, Ankara University School of Medicine, Ankara, Türkiye
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13
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Why are pediatric urologists unable to predict renal deterioration using urodynamics? A focused narrative review of the shortcomings of the literature. J Pediatr Urol 2022; 18:493-498. [PMID: 35817657 DOI: 10.1016/j.jpurol.2022.05.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/29/2022] [Accepted: 05/19/2022] [Indexed: 11/22/2022]
Abstract
In this focused narrative review we set out to review the current literature addressing the utilization of UDS in patients with spina bifida (SB). We specifically analyzed 6 urodynamic parameters and their roles as predictors of upper tract deterioration in pediatric SB patients. The material available did not allow a systematic analysis or the usage of metanalysis methodology, due to the predominance of small retrospective cohorts, and high heterogeneity. We identified 10 retrospective chart reviews that met our study criteria. The results of each of these papers, as well as other studies deemed relevant to the discussion, are included in our narrative review of the literature. We summarize the current literature, offer explanations for divergences in opinion, and identify future research directions and emerging solutions with a focus on machine learning.
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14
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Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists. Urol Clin North Am 2022; 49:65-117. [PMID: 34776055 PMCID: PMC9147289 DOI: 10.1016/j.ucl.2021.07.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The growth and adoption of artificial intelligence has led to impressive results in urology. As artificial intelligence grows more ubiquitous, it is important to establish artificial intelligence literacy in the workforce. To this end, we present a narrative review of the literature of artificial intelligence and machine learning in urology and propose a checklist of reporting standards to improve readability and evaluate the current state of the literature. The listed article demonstrated heterogeneous reporting of methodologies and outcomes, limiting generalizability of research. We hope that this review serves as a foundation for future evaluation of medical research in artificial intelligence.
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15
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Hobbs KT, Choe N, Aksenov LI, Reyes L, Aquino W, Routh JC, Hokanson JA. Machine Learning for Urodynamic Detection of Detrusor Overactivity. Urology 2022; 159:247-254. [PMID: 34757048 PMCID: PMC8865755 DOI: 10.1016/j.urology.2021.09.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 09/20/2021] [Accepted: 09/22/2021] [Indexed: 01/03/2023]
Abstract
OBJECTIVE To develop a machine learning algorithm that identifies detrusor overactivity (DO) in Urodynamic Studies (UDS) in the spina bifida population. UDS plays a key role in assessment of neurogenic bladder in patients with spina bifida. Due to significant variability in individual interpretations of UDS data, there is a need to standardize UDS interpretation. MATERIALS AND METHODS Patients who underwent UDS at a single pediatric urology clinic between May 2012 and September 2020 were included. UDS files were analyzed in both time and frequency domains, varying inclusion of vesical, abdominal, and detrusor pressure channels. A machine learning pipeline was constructed using data windowing, dimensionality reduction, and support vector machines. Models were designed to detect clinician identified detrusor overactivity. RESULTS Data were extracted from 805 UDS testing files from 546 unique patients. The generated models achieved good performance metrics in detecting DO agreement with the clinician, in both time- and frequency-based approaches. Incorporation of multiple channels and data windowing improved performance. The time-based model with all 3 channels had the highest area under the curve (AUC) (91.9 ± 1.3%; sensitivity: 84.2 ± 3.8%; specificity: 86.4 ± 1.3%). The 3-channel frequency-based model had the highest specificity (AUC: 90.5 ± 1.9%; sensitivity: 68.3 ± 5.3%; specificity: 92.9 ± 1.1%). CONCLUSION We developed a promising proof-of-concept machine learning pipeline that identifies DO in UDS. Machine-learning-based predictive modeling algorithms may be employed to standardize UDS interpretation and could potentially augment shared decision-making and improve patient care.
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Affiliation(s)
- Kevin T. Hobbs
- Division of Urologic Surgery, Duke University Medical Center, Durham, NC
| | - Nathaniel Choe
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
| | - Leonid I. Aksenov
- Division of Urologic Surgery, Duke University Medical Center, Durham, NC
| | - Lourdes Reyes
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC
| | - Wilkins Aquino
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC
| | - Jonathan C. Routh
- Division of Urologic Surgery, Duke University Medical Center, Durham, NC
| | - James A. Hokanson
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI,Corresponding Author. James Hokanson, PhD, Biomedical Engineering, Translational and Biomedical Research Center, 8701 W Watertown Plank Road, Milwaukee, WI, 53226,
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Yerkes EB, Cheng EY, Wiener JS, Austin JC, Tu DD, Joseph DB, Routh JC, Tanaka ST. Translating pediatric urodynamics from clinic into collaborative research: Lessons and recommendations from the UMPIRE study group. J Pediatr Urol 2021; 17:716-725. [PMID: 34412976 DOI: 10.1016/j.jpurol.2021.05.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 05/02/2021] [Accepted: 05/05/2021] [Indexed: 10/21/2022]
Abstract
Pediatric patients present unique challenges in the performance and interpretation of urodynamic studies. Interpretation of urodynamics to guide clinical management at an institutional level is accepted as reliable. Challenges arise however when multi-site collaborations incorporate urodynamics into study design to determine primary or secondary outcomes or to direct decision-making. Although standardized terminology has been established by ICCS, the application of this shared language to performance and interpretation of pediatric urodynamics to across multiple sites may not be intuitive or reliable. With a primary goal of defining the care necessary to protect future renal function, the UMPIRE protocol (Urologic Management to Preserve Initial REnal function) utilizes a urodynamics-based risk stratification to determine medical management for infants with myelomeningocele. Iterative changes in the protocol are based upon the clinical progress of the enrolled children. Despite a team experienced in subtleties of urodynamics and despite efforts to minimize variability across sites, the UMPIRE study group identified several areas in which the language of urodynamics required additional clarification or creation of more explicit definitions to standardize performance and interpretation across sites. This article reviews the foundations of current urodynamics practice, describes limitations and challenges unique to pediatric studies, and the shares the humble lessons learned by the UMPIRE study group on their journey toward standardized urodynamic language for management of infants and children with myelomeningocele.
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Affiliation(s)
- Elizabeth B Yerkes
- Ann and Robert H. Lurie Children's Hospital of Chicago, 225 E Chicago Avenue, Pediatric Urology #24, Chicago, IL, 60611, USA.
| | - Earl Y Cheng
- Ann and Robert H. Lurie Children's Hospital of Chicago, 225 E Chicago Avenue, Pediatric Urology #24, Chicago, IL, 60611, USA
| | - John S Wiener
- Duke University Medical Center, Box 3831, Durham, NC, 27710, USA
| | | | - Duong D Tu
- Texas Children's Hospital / Baylor College of Medicine, 6701 Fannin St, Suite 620, Houston, TX, 77030, USA
| | - David B Joseph
- Children's of Alabama, 1600 7th Ave South, Lowder 318, Birmingham, AL, 35233, USA
| | - Jonathan C Routh
- Duke University Medical Center, Box 3831, Durham, NC, 27710, USA
| | - Stacy T Tanaka
- Vanderbilt University Medical Center, 2200 Children's Way. 4102 DOT, Nashville, TN, 37232, USA
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Bentellis I, Guérin S, Khene ZE, Khavari R, Peyronnet B. Artificial intelligence in functional urology: how it may shape the future. Curr Opin Urol 2021; 31:385-390. [PMID: 33989231 DOI: 10.1097/mou.0000000000000888] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
PURPOSE OF REVIEW The aim of the present manuscript is to provide an overview on the current state of artificial intelligence (AI) tools in either decision making, diagnosis, treatment options, or outcome prediction in functional urology. RECENT FINDINGS Several recent studies have shed light on the promising potential of AI in functional urology to investigate lower urinary tract dysfunction pathophysiology but also as a diagnostic tool by enhancing the existing evaluations such as dynamic magnetic resonance imaging or urodynamics. AI may also improve surgical education and training because of its automated performance metrics recording. By bringing prediction models, AI may also have strong therapeutic implications in the field of functional urology in the near future. AI may also be implemented in innovative devices such as e-bladder diary and electromechanical artificial urinary sphincter and could facilitate the development of remote medicine. SUMMARY Over the past decade, the enthusiasm for AI has been rising exponentially. Machine learning was well known, but the increasing power of processors and the amount of data available has provided the platform for deep learning tools to expand. Although the literature on the applications of AI technology in the field of functional urology is relatively sparse, its possible uses are countless especially in surgical training, imaging, urodynamics, and innovative devices.
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Affiliation(s)
- Imad Bentellis
- Department of Urology, University of Nice-Sophia Antipolis, Nice
| | | | | | - Rose Khavari
- Department of Urology, Houston Methodist Hospital, Houston, Texas, USA
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