Development and Validation of a Deep Learning System for Sound-based Prediction of Urinary Flow

Lee, Han Jie; Aslim, Edwin Jonathan; Balamurali, B T; Ng, Lynn Yun Shu; Kuo, Tricia Li Chuen; Lin, Cindy Ming Ying; Clarke, Christopher Johann; Priyadarshinee, Prachee; Chen, Jer‐Ming; Ng, Lay Guat

doi:10.1016/j.euf.2022.06.011

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…A sound-based deep learning algorithm (Audioflow®) was developed to estimate uroflowmetry parameters and identify abnormal urinary flow patterns, 15 showing an agreement with conventional uroflowmetry for maximum flow, average flow, and voided volume of 77%, 85%, and 84%, respectively. For detection of abnormal uroflow (according to experts' criteria) the area under the curve (AUC) was 0.89.…”

Section: Applying ML To Uroflowmetry and Cystometry Datamentioning

confidence: 97%

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI‐RS 2023

Gammie,

Arlandis,

Couri

et al. 2023

Neurourology and Urodynamics

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IntroductionA “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.MethodsExamples 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.ResultsML 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.ConclusionsML 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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Section: Applying ML To Uroflowmetry and Cystometry Datamentioning

confidence: 97%

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI‐RS 2023

Gammie,

Arlandis,

Couri

et al. 2023

Neurourology and Urodynamics

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Applications of machine learning in urodynamics: A narrative review

Liu,

Zhong,

Gao

et al. 2024

Neurourology and Urodynamics

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BackgroundMachine 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.MethodsWe 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.ResultsThe 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.ConclusionsAt 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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The Use of Uroflowmetry as a Diagnostic Test

Sinha

2024

Curr Urol Rep

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Development and Validation of a Deep Learning System for Sound-based Prediction of Urinary Flow

Cited by 5 publications

References 17 publications

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI‐RS 2023

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI‐RS 2023

Applications of machine learning in urodynamics: A narrative review

The Use of Uroflowmetry as a Diagnostic Test

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