Voiding Dysfunction

The following models are related to voiding dysfunction and detrusor overactivity.

Weaver et al. (2025)

Hydronephrosis from VUDS in spina bifida Article DOI https://dx.doi.org/10.1097/JU.0000000000004547 Objective To predict incident hydronephrosis in patients with spina bifida using videourodynamics data AI Approach Random forrest Data Source(s) Single institutional series (554 patients) Model Input Four models using (1) prospectively collected clinical characteristics, (2) urodynamic pressure-volume recordings, (3) fluoroscopic imaging, and (4) risk prediction scores…

Yuan et al. (2024)

Bladder compliance in urodynamics, Voiding Dysfunction. Article DOI https://doi.org/10.1080/10255842.2023.2301414 Objective To develop an efficient bladder compliance screen approach before UDS AI Approach CNN Data Source(s) Single institutional series (805 urodynamic series) Model Input 15 features from urodynamic study (time-based and frequency-based), after principal component analysis Model Outcome Detrusor overactivity (Time-based and frequency-based) Model Metrics Time-based…

Ge et al. (2022)

Bladder compliance, Voiding Dysfunction. Article DOI https://doi.org/10.1016/j.compbiomed.2021.105173 Objective To classify and predict bladder compliance from a novel method that measures intravesical pressure during the VCUG examination without extra UDS. AI Approach SVM, RF, Logistic Regression, Perceptron, XGBoost, and Naive Bayes Data Source(s) Single institutional series (52 patients) Model Input Time-domain and wavelet features extracted from…

Weaver et al. (2023b)

To categorize bladder dysfunction in Spina Bifida, LUTD Article DOI https://doi.org/10.1097/JU.0000000000003267 Objective To develop deeplearning models of VUDS to categorize severity of bladder dysfunction in patients with spina bifida AI Approach Individual and ensemble model (Random forest, CNN from tracing and imaging, respectively) Data Source(s) Single institutional series (306 VUDS from 256 patients) Model Input…

Wang et al. (2021)

Detrusor Overactivity, Other Article DOI https://doi.org/10.1002/nau.24578 Objective To identify detrusoroveractivity fromurodynamic studies AI Approach Manifold learning Data Source(s) Institutional series (799 urodynamic studies) Model Input Demographics, raw tracings of vesical pressure, abdominal pressure, detrusor pressure, infused volume, annotations Model Outcome Detrusor Overactivity Model Metrics AUROC = 0.84 Model Usability NA AI = Artificial Intelligence, AUROC…

Hobbs et al. (2022)

Detrusor Overactivity, Other Article DOI https://doi.org/10.1016/j.urology.2021.09.027 Objective To identify detrusor overactivity from urodynamic studies in the spina bifida population AI Approach SVM, Dimensionality reduction Data Source(s) Institutional series (805 urodynamic studies) Model Input 15 features from urodynamicstudy (time-based and frequency-based), afterprincipal component analysis Model Outcome Detrusor Overactivity Model Metrics Time-based detrusor overactivity: AUROC = 0.92Frequency-based…