ReviewNeurourology and urodynamics2024
Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI-RS 2023.
Review in Neurourology and urodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Artificial intelligence and machine learning in neurogenic lower urinary tract dysfunction and spinal cord injury: current status, emerging technologies, and ethical perspectives.Frontiers in urology · 2026Article
- Can unsupervised machine learning gain new insights into urodynamic pressure flow pattern analysis?BJU international · 2026Article
- Envisioning the Future of AI in Urodynamics: Exploratory Interviews With Experts.Neurourology and urodynamics · 2025Article
- Evaluating the utility of ChatGPT in addressing conceptual and non-conceptual questions related to urodynamic quality control and trace analysis.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.