133 Can We Spare Pressure-Flow Studies? Uroflowmetry Pattern Recognition Using Computational Processing: Preliminary Results.

Colet, Oriol; Catalá, O Del Tejo; Navarro-Cerdán, J; Morán, E; Marset, J Bolón; Perez-Cortes, J; Llobet, R; Martínez-Cuenca, E; Bonillo, M; Arlandis, S

doi:10.1016/j.cont.2022.100245

Cited by 1 publication

(2 citation statements)

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“…16 A different approach used raw data from male LUTS patients' uroflowmetries to predict DU, applying the partial least squares regression algorithm, with an AUC of 0.80 and an optimum sensitivity of 73% and specificity of 85%. 17 Such studies have used both flow curve shape and standard uroflowmetry parameters, but these algorithms still have not reached adequate robustness and reliability to substitute for conventional uroflowmetry and pressure-flow studies.…”

Section: Potential For Reduction Of Variability In Diagnosis Between ...mentioning

confidence: 99%

“…A convolutional neural network (VGG16 model) was applied to conventional uroflowmetries of a cohort of male patients with LUTS to predict bladder outlet obstruction (BOO) and detrusor underactivity (DU), showing an AUC of 0.73 and 0.72, respectively 16 . A different approach used raw data from male LUTS patients' uroflowmetries to predict DU, applying the partial least squares regression algorithm, with an AUC of 0.80 and an optimum sensitivity of 73% and specificity of 85% 17 . Such studies have used both flow curve shape and standard uroflowmetry parameters, but these algorithms still have not reached adequate robustness and reliability to substitute for conventional uroflowmetry and pressure‐flow studies.…”

Section: Applying ML To Uroflowmetry and Cystometry Datamentioning

confidence: 99%

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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: Potential For Reduction Of Variability In Diagnosis Between ...mentioning

confidence: 99%

Section: Applying ML To Uroflowmetry and Cystometry Datamentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

133 Can We Spare Pressure-Flow Studies? Uroflowmetry Pattern Recognition Using Computational Processing: Preliminary Results.

Cited by 1 publication

References 3 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

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