RESUMEN
INTRODUCTION: A "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. METHODS: Examples 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. RESULTS: ML 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. CONCLUSIONS: ML 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.
RESUMEN
INTRODUCTION: The evidence basis for therapy selection in women who have failed primary stress urinary incontinence (SUI) surgery is limited. The ICI-RS group discussed the available data at its meeting in June 2023, particularly the anatomical characteristics as assessed using magnetic resonance imaging (MRI) and ultrasound (US) modalities, functional characteristics associated with storage and voiding urodynamic assessment, as well as the patient characteristics that might influence outcomes. This paper summarizes the evidence base that supported these discussions and offers the basis for research proposals for future groups. METHODS: A literature search of MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials was performed, and the data is presented. Research questions are based on the knowledge gaps highlighted. RESULTS: Possible MRI parameters that may influence outcomes were striated urethral sphincter volume, bladder and proximal urethral funneling, pubo-urethral ligament integrity, distance of the bladder neck below the pubococcygeal line, posterior urethra-vesical angle, and bladder neck to levator ani distance. US parameters included sling distance to the urethral lumen and pubis, sling position, bladder neck mobility, and lateral arm asymmetry, twisting, or curling. Urodynamic parameters included detrusor overactivity, Valsalva leak point pressure, maximum urethral closure pressure, and bladder outlet obstruction. Important patient parameters included body mass index, age, and previous interventions. CONCLUSIONS: Identifying and quantifying causative factors in patients with recurrent SUI, that allow clinicians to modify subsequent treatment choices and techniques may help reduce treatment failure and complications. Formulating algorithms is the next step in optimizing patient counseling, surgical selection, and healthcare allocation.