Currently accepted at: JMIR Research Protocols
Date Submitted: Aug 20, 2026
Date Accepted: Oct 15, 2026
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/110023
The final accepted version (not copyedited yet) is in this tab.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Development and Validation of a Multimodal Artificial Neural Network Model for the Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men with Lower Urinary Tract Symptoms: Protocol for a Diagnostic Study
ABSTRACT
Background:
Lower urinary tract symptoms (LUTS) are common in men and increase with age. Bladder outlet obstruction (BOO) and detrusor underactivity (DU) have overlapping symptoms and uroflowmetry findings but require different treatment. Pressure-flow study (PFS) is the reference standard, but its invasiveness, cost, and limited availability restrict routine use. A multimodal artificial neural network (ANN) may support pre-PFS estimation of BOO and DU in selected men with LUTS.
Objective:
To develop and validate a multimodal ANN model for diagnosing BOO and DU in men with LUTS, using urodynamic PFS as the reference standard.
Methods:
This two-phase, mixed retrospective–prospective diagnostic accuracy and prediction-model study comprises retrospective model development and prospective multicenter external validation. The development cohort will consist of men with LUTS from Persahabatan General Hospital who underwent clinical assessment, IPSS, comorbidity assessment, uroflowmetry, prostate ultrasonography, cystoscopy, and PFS. External validation will be performed using independent multicenter datasets from four tertiary referral hospitals. A multimodal ANN with intermediate fusion will combine clinical variables, uroflowmetric parameters, uroflowmetry waveform signals, structured prostate ultrasonography measurements, and cystoscopic images through parallel branches. Performance will be evaluated using accuracy, sensitivity, specificity, predictive values, precision, recall, F1-score, area under the receiver operating characteristic curve, calibration analysis, and decision curve analysis.
Results:
The study is in the protocol development stage. Development of the computational pipeline and web-based prototype began in June 2026, with results expected by the end of 2026.
Conclusions:
The proposed multimodal ANN may support pre-PFS differentiation of BOO and DU in selected men with LUTS and facilitate mechanism-based clinical decision-making.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.