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Rickey LM, Mueller ER, Newman DK, Markland AD, Falke C, Rudser K, Smith AL, Mueller MG, Lowder JL, Lukacz ES. Reliability of Uroflowmetry Pattern Interpretation in Adult Women. Neurourol Urodyn 2024; 43:2084-2092. [PMID: 39264028 DOI: 10.1002/nau.25584] [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: 05/14/2024] [Revised: 07/03/2024] [Accepted: 08/10/2024] [Indexed: 09/13/2024]
Abstract
INTRODUCTION Uroflowmetry is often used to assess lower urinary tract symptoms (LUTS). Criteria for characterization of flow patterns are not well established, and subjective interpretation is the most common approach for flow curve classification. We assessed the reliability of uroflowmetry curve interpretation in adult women. MATERIALS AND METHODS Uroflowmetry studies were obtained in 296 women who participated in an observational cohort study. Four investigators with expertise in female LUTS and urodynamics reviewed and categorized each tracing for interrater reliability. A random subset of 50 tracings was re-reviewed by each investigator for intrarater reliability. The uroflowmetry tracings were rated using categories of continuous, continuous fluctuating, interrupted, and prolonged. Other parameters included flow rate, voided volume, time to maximum flow, and voiding time. Agreement between raters is summarized with kappa (k) statistics and percentage where at least three raters agreed. RESULTS The mean age of participants was 44.8 ± 18.3 years. Participant age categories were 18-24 years: 20%; 25-34 years: 17%; 35-64 years: 42%; 65+ years: 18%. Nine percent described their race as Asian, 31% Black, 62% White, and 89% were of non-Hispanic ethnicity. The interrater reliability was highest for the continuous flow category (k = 0.65), 0.47 for prolonged, 0.41 for continuous fluctuating, and 0.39 for interrupted flow curves. Agreement among at least three raters occurred in 74.3% of uroflow curves (69% for continuous, 33% for continuous fluctuating, 23% for interrupted, and 25% for prolonged). For intrarater reliability, the mean k was 0.72 with a range of 0.57-0.85. CONCLUSIONS Currently accepted uroflowmetry pattern categories have fair to moderate interrater reliability, which is lower for flow curves that do not meet "continuous" criteria. Given the subjective nature of interpreting uroflowmetry data, more consistent and clear parameters may enhance reliability for use in research and as a screening tool for LUTS and voiding dysfunction. TRIAL REGISTRATION Parent trial: Validation of Bladder Health Instrument for Evaluation in Women (VIEW); ClinicalTrials.gov ID: NCT04016298.
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Affiliation(s)
- Leslie M Rickey
- Department of Urology & Obstetrics and Gynecology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Elizabeth R Mueller
- Department of Obstetrics and Gynecology, Loyola University Chicago, Chicago, Illinois, USA
| | - Diane K Newman
- Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Alayne D Markland
- Department of Medicine, Division of Gerontology, Geriatrics and Palliative Care, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Chloe Falke
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Kyle Rudser
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ariana L Smith
- Division of Urology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Margaret G Mueller
- Department of Obstetrics and Gynecology, University of Chicago Medicine, Chicago, Illinois, USA
| | - Jerry L Lowder
- Department of Obstetrics and Gynecology, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA
| | - Emily S Lukacz
- Division of Female Pelvic Medicine & Reconstructive Surgery, UC San Diego, San Diego, California, USA
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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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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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Di Bello F, Scandurra C, Muzii B, Colla’ Ruvolo C, Califano G, Mocini E, Creta M, Napolitano L, Morra S, Fraia A, Bochicchio V, Salzano G, Vaira LA, Mangiapia F, Motta G, Motta G, Maldonato NM, Longo N, Cantone E. Are Excessive Daytime Sleepiness and Lower Urinary Tract Symptoms the Triggering Link for Mental Imbalance? An Exploratory Post Hoc Analysis. J Clin Med 2023; 12:6965. [PMID: 38002580 PMCID: PMC10672561 DOI: 10.3390/jcm12226965] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/04/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Both lower urinary tract symptoms (LUTS) and excessive daytime sleepiness (EDS) could negatively impair the patients' quality of life, increasing the sensitivity to psychological distress that results in mental health disorders. The relationships of both urinary and respiratory domains with psychological distress in obstructive sleep apnea patients is still underestimated. METHODS This study was a post hoc analysis of a web-based Italian survey, which included 1998 participants. Three hierarchical multiple linear regression analyses with psychological distress as dependent variable were performed on the study of 1988 participants enrolled in the final analysis. Cohen's f2 was used for the assessment of the effect size. RESULTS From the hierarchical multiple linear regression analyses, it emerged that the final statistical model (including sociodemographic characteristics, comorbidities, perceived urinary function, and excessive daytime sleepiness) for all dimensions accounted for 16.7% of the variance in psychological distress, with a medium effect size (f2 = 0.15). CONCLUSIONS People reported psychological distress was impaired by the presence of LUTS and EDS. Specifically, our study showed that higher levels of distress were scored especially in young women exhibiting urinary symptoms and with high values of daytime sleepiness.
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Affiliation(s)
- Francesco Di Bello
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Cristiano Scandurra
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Benedetta Muzii
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Claudia Colla’ Ruvolo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Gianluigi Califano
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Edoardo Mocini
- Department of Experimental Medicine, Sapienza University, 00185 Rome, Italy;
| | - Massimiliano Creta
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Luigi Napolitano
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Simone Morra
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Agostino Fraia
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Vincenzo Bochicchio
- Department of Humanistic Studies, University of Calabria, 87036 Rende, Italy;
| | - Giovanni Salzano
- Maxillofacial Surgery Operative Unit, University Hospital of Naples “Federico II”, 80131 Naples, Italy;
| | - Luigi Angelo Vaira
- Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy;
| | - Francesco Mangiapia
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Gaetano Motta
- ENT Unit, Department of Mental, Physical Health and Preventive Medicine, University of Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.M.); (G.M.)
| | - Giovanni Motta
- ENT Unit, Department of Mental, Physical Health and Preventive Medicine, University of Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.M.); (G.M.)
| | - Nelson Mauro Maldonato
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Nicola Longo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
| | - Elena Cantone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.D.B.); (C.S.); (C.C.R.); (G.C.); (M.C.); (L.N.); (S.M.); (A.F.); (F.M.); (N.M.M.); (N.L.); (E.C.)
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Wang J, Ren L, Liu X, Liu J, Ling Q. Underactive Bladder and Detrusor Underactivity: New Advances and Prospectives. Int J Mol Sci 2023; 24:15517. [PMID: 37958499 PMCID: PMC10648240 DOI: 10.3390/ijms242115517] [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: 09/18/2023] [Revised: 10/20/2023] [Accepted: 10/22/2023] [Indexed: 11/15/2023] Open
Abstract
Underactive bladder (UAB) is a prevalent but under-researched lower urinary tract symptom that typically occurs alongside detrusor underactivity (DU). Unlike UAB, DU is a urodynamic diagnosis which the International Continence Society (ICS) defines as "a contraction of reduced strength and/or duration, resulting in prolonged bladder emptying and/or a failure to achieve complete bladder emptying within a normal time span". Despite the widespread prevalence of UAB/DU, there are significant gaps in our understanding of its pathophysiological mechanisms, diagnosis, and treatment compared with overactive bladder (OAB) and detrusor overactivity (DO). These gaps are such that clinicians regard UAB/DU as an incurable condition. In recent years, the understanding of UAB has increased. The definition of UAB has been clarified, and the diagnostic criteria for DU have been considered more comprehensively. Meanwhile, a number of non-invasive diagnostic methods have also been reported. Clinical trials involving novel drugs, electrical stimulation, and stem cell therapy have shown promising results. Therefore, this review summarizes recent reports on UAB and DU and highlights the latest advances in their diagnosis and treatment.
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Affiliation(s)
- Jiaxin Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (J.W.)
- Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Lida Ren
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (J.W.)
- Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xinqi Liu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (J.W.)
- Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jihong Liu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (J.W.)
- Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Qing Ling
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (J.W.)
- Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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