Observational studyWorld journal of urology2025
Artificial inteligence reading of cystometric traces provides good correlation with human diagnosis.
Observational study in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimUrodynamic studies are essential for diagnosing lower urinary tract dysfunction but are expert-dependent and time-consuming. Artificial intelligence (AI), notably machine learning (ML) and deep learning (DL) may help automate and standardize interpretation, reducing inter-observer variability and improving efficiency.
objectiveTo evaluate the correlation between artificial intelligence (AI) based classification and human expert diagnosis of detrusor overactivity (DO) in cystometry (CMG), with explicit handling of artifacts and quantification of parameters. STUDY
designRetrospective, single-center, observational diagnostic-accuracy (cross-sectional) study with a consecutive cohort of adults who underwent cystometry in 2023, in which AI outputs were compared with a reference standard (three-urologist consensus). We evaluated 517 cystometry (CMG) tracings: 200 used to train AI models and 317 reserved for testing. Two approaches were assessed: (i) image-based CNN-VGG16 deep learning, which achieved 75% accuracy for detecting detrusor overactivity (DO) but did not yield quantitative metrics and (ii) wavelet-based ML (Daubechies transforms), which improved accuracy to 84.2%, with 82.6% specificity and 86.3% sensitivity, while providing detailed contraction descriptors. An Isolation Forest anomaly-detection stage identified and managed artifacts (e.g., coughs, open lines, catheter movement). Integrating signal processing (time-frequency denoising and rule-based thresholds) with AI classification supported robust CMG event recognition, enabling clearer identification of DO, estimation of bladder compliance from DO-free segments, and mitigation of artifacts. Both branches produced classifications in less than 20 s per study.
conclusionCombining algorithmic outputs with expert supervision could deliver practical, faster, and more reproducible urodynamic reporting, while preserving clinical accountability and transparency and generalizability.
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Registered trials
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