ReviewAmerican journal of translational research2024
Advancements in artificial intelligence for pelvic floor ultrasound analysis.
Review in American journal of translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction model for postpartum stress urinary incontinence risk: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- AI-Enhanced 3D Transperineal Ultrasound: Advancing Biometric Measurements for Precise Prolapse Severity Assessment.Bioengineering (Basel, Switzerland) · 2025Article
- An optimized deep learning model based on transperineal ultrasound images for precision diagnosis of female stress urinary incontinence.Frontiers in medicine · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pelvic floor ultrasound provides a clear depiction of pelvic floor structures and their spatial anatomical relationships, enabling enhanced observation of pelvic organ function and position. The integration of artificial intelligence (AI) into medical imaging has revolutionized the automatic analysis of imaging data, offering efficient and accurate preprocessing and analysis. This technological advance addresses challenges associated with traditional pelvic floor ultrasound, such as reliance on operator's experience, time-intensive manual measurements, and significant potential for human error. Current AI applications in pelvic floor ultrasound encompass automatic measurement of the angle of progress (AOP), automatic segmentation of the levator hiatus (LH), and automatic identification of the levator ani muscle (LAM). AI excels in mimicking human analysis, distilling patterns from reorganized data. This paper, grounded in a comprehensive literature review, outlines the principal aspects of pelvic floor ultrasound and its augmentation through AI, highlighting the application value and progress of AI in this field.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.