Observational studyTomography (Ann Arbor, Mich.)2022
Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound.
Observational study in Tomography (Ann Arbor, Mich.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Graph Attention Network-Based Multimodal Auxiliary Intelligent Grading Model for Uterine Prolapse Severity.International urogynecology journal · 2026Article
- A Dual-Task Deep-Learning Model with Fused Ultrasound Images for Simultaneous Typing and Grading of Cystocele.International urogynecology journal · 2026Article
- [Deep learning models based on fused ultrasound images for the assessment of cystocele in women].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Automated transperineal ultrasound analysis using deep learning for pelvic floor dysfunction assessment after total hysterectomy.Frontiers in oncology · 2026Article
- Artificial Intelligence to Determine Correct Midsagittal Plane in Dynamic Transperineal Ultrasound.Journal of clinical ultrasound : JCU · 2025Observational
- Epidural Anesthesia and Pelvic Floor Outcomes in Primiparas: A Retrospective Transperineal Ultrasound Study.International urogynecology journal · 2025Article
- Applicability of Deep Learning to Dynamically Identify the Different Organs of the Pelvic Floor in the Midsagittal Plane.International urogynecology journal · 2024Observational
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We want to describe a model that allows the use of transperineal ultrasound to define the probability of experiencing uterine prolapse (UP). This was a prospective observational study involving 107 patients with UP or cervical elongation (CE) without UP. The ultrasound study was performed using transperineal ultrasound and evaluated the differences in the pubis−uterine fundus distance at rest and with the Valsalva maneuver. We generated different multivariate binary logistic regression models using nonautomated methods to predict UP, including the difference in the pubis−uterine fundus distance at rest and with the Valsalva maneuver. The parameters were added progressively according to their simplicity of use and their predictive capacity for identifying UP. We used two binary logistic regression models to predict UP. Model 1 was based on the difference in the pubis−uterine fundus distance at rest and with the Valsalva maneuver and the age of the patient [AUC: 0.967 (95% CI, 0.939−0.995; p < 0.0005)]. Model 2 used the difference in the pubis−uterine fundus distance at rest and with the Valsalva maneuver, age, avulsion and ballooning (AUC: 0.971 (95% CI, 0.945−0.997; p < 0.0005)). In conclusion, the model based on the difference in the pubis−uterine fundus distance at rest and with the Valsalva maneuver and the age of the patient could predict 96.7% of patients with UP.
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