ArticleScientific reports2023
Predicting female pelvic tilt and lumbar angle using machine learning in case of urinary incontinence and sexual dysfunction.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed, 74 citations in OpenAlex.
- Modeling predictors of incomplete antenatal care utilization among reproductive-age women in Ethiopia using machine learning algorithms and SHAP interpretation.PLOS digital health · 2026Article
- Sexual dysfunction after elective spine surgery: A systematic review identifying knowledge gaps and clinical implications.Brain & spine · 2026Review
- Multicenter Validation of Video-based Deep Learning to Evaluate Defecation Patterns on 3-dimensional High-definition Anorectal Manometry.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026Article
- Article
- Using convolutional neural networks with late fusion to predict heart disease.Scientific reports · 2025Article
- Are Machine Learning methods effective in detecting undiagnosed atrial fibrillation in primary care settings using electronic health records? A systematic review.PLOS digital health · 2025Article
- Attention over vulnerable brain regions associating cerebral palsy disorder and biological markers.Journal of advanced research · 2025Article
- Classification of musculoskeletal pain using machine learning.Scientific reports · 2025Article
- Experience of Cardiovascular and Cerebrovascular Disease Surgery Patients: Sentiment Analysis Using the Korean Bidirectional Encoder Representations from Transformers (KoBERT) Model.JMIR medical informatics · 2025Article
- Predictive optimization using long short-term memory for solar PV and EV integration in relatively cold climate energy systems with a regional case study.Scientific reports · 2025Article
- Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.Abdominal radiology (New York) · 2025Review
- Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations.Heliyon · 2025Article
- Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study.Scientific reports · 2025Article
- Article
- Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Article
- Article
- A comprehensive learning based swarm optimization approach for feature selection in gene expression data.Heliyon · 2024Article
- Machine Learning-Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals.JMIRx med · 2024Article
- A comprehensive survey on exploring and analyzing COVID-19 mobile apps: Meta and exploratory analysis.Heliyon · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Urinary incontinence (UI) is defined as any uncontrolled urine leakage. Pelvic floor muscles (PFM) appear to be a crucial aspect of trunk and lumbo-pelvic stability, and UI is one indication of pelvic floor dysfunction. The evaluation of pelvic tilt and lumbar angle is critical in assessing the alignment and posture of the spine in the lower back region and pelvis, and both of these variables are directly related to female dysfunction in the pelvic floor. UI affects a significant number of women worldwide and can have a major impact on their quality of life. However, traditional methods of assessing these parameters involve manual measurements, which are time-consuming and prone to variability. The rehabilitation programs for pelvic floor dysfunction (FSD) in physical therapy often focus on pelvic floor muscles (PFMs), while other core muscles are overlooked. Therefore, this study aimed to predict the activity of various core muscles in multiparous women with FSD using multiple scales instead of relying on Ultrasound imaging. Decision tree, SVM, random forest, and AdaBoost models were applied to predict pelvic tilt and lumbar angle using the train set. Performance was evaluated on the test set using MSE, RMSE, MAE, and R
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.