ArticleInternational urogynecology journal2022
Artificial intelligence models derived from 2D transperineal ultrasound images in the clinical diagnosis of stress urinary incontinence.
Article in International urogynecology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Machine learning-based prediction model for postpartum stress urinary incontinence risk: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Post-Surgical Evaluation in Stress Urinary Incontinence by Transperineal Ultrasound and Quality of Life Questionnaire-A Retrospective Study.Diagnostics (Basel, Switzerland) · 2026Article
- Transperineal Ultrasound Measurement of Retrovesical Angle at Rest for Assessing Severity of Stress Urinary Incontinence: A Cross-Sectional Study.Journal of clinical medicine · 2026Article
- Automated Evaluation of Female Pelvic Organ Descent on Transperineal Ultrasound: Model Development and Validation.International urogynecology journal · 2025Article
- Innovations in Stress Urinary Incontinence: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects.Diagnostics (Basel, Switzerland) · 2025Review
- Prediction Models of Microinvasive Cervical Cancer in High-Grade Squamous Intraepithelial Lesion Treatment by Loop Electrosurgical Excision Procedure.Risk management and healthcare policy · 2025Article
- Evaluation of Peri-Operative Outcomes after Prostatic Urethral Lift with Emphasis on Urodynamic Changes, Symptom Improvement and Sexual Function.Diagnostics (Basel, Switzerland) · 2024Article
- Validation of a built-in software in automatically reconstructing the tomographic images of the levator ani muscle.International urogynecology journal · 2024Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
introduction and hypothesisThe aim of the study was to develop artificial intelligence (AI) algorithms using 2D transperineal ultrasound (TPUS) static images to simplify the clinical process of diagnosing stress urinary incontinence (SUI) in practice.
methodsThe study involved 400 patients in total, including 265 SUI patients and 135 non-SUI patients who underwent a routine clinical evaluation process by urologists and TPUS. They were classified into different groups based on the International Consultation on Incontinence Questionnaire (ICIQ) to assess the impact of inconvenience on patients' lives. Four AI models were developed by 2D TPUS images: Model A (a single-mode model based on Valsalva maneuver images to classify G-0, G-1, and G-2); Model B (a dual-mode model based on Valsalva maneuver and resting state images to classify G-0, G-1, and G-2); Model C (a single-mode model based on Valsalva maneuver images to classify G-2 and G-01); Model D (a dual-mode model based on Valsalva maneuver and resting state images to classify G-2 and G-01). The performance of the four models was evaluated by confusion matrices and the area under the receiver-operating characteristic curve (AUC).
resultsThe dual-mode model based on the Valsalva maneuver and resting-state images (Model D) had a higher accuracy of 86.3% and an AUC of 0.922, which was significantly higher than the AUCs of the other three models: 0.771, 0.862, and 0.827.
conclusionsThe AI algorithm using 2D TPUS static images of the Valsalva maneuver and resting state may be a promising tool in the diagnosis of SUI patients in to relieve clinical processes in practice given its ease of use in clinical applications.
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