ArticleScientific reports2024
Revolutionizing core muscle analysis in female sexual dysfunction based on machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 52 citations in OpenAlex.
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- 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
- Particle swarm optimization framework for Parkinson's disease prediction.PeerJ. Computer science · 2025Article
- Machine learning insights into scapular stabilization for alleviating shoulder pain in college students.Scientific reports · 2024Article
- The Intersection of Artificial Intelligence, Wearable Devices, and Sexual Medicine.Current urology reports · 2024Review
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- A comprehensive learning based swarm optimization approach for feature selection in gene expression data.Heliyon · 2024Article
- Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain.Scientific reports · 2024Article
- Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment.Scientific reports · 2024Article
- Ultrasound imaging of core muscles activity in multiparous women with vaginal laxity: a cross-sectional study.Scientific reports · 2024Observational
- YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation.Digital healthArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study is to investigate the role of core muscles in female sexual dysfunction (FSD) and develop comprehensive rehabilitation programs to address this issue. We aim to answer the following research questions: what are the roles of core muscles in FSD, and how can machine and deep learning models accurately predict changes in core muscles during FSD? FSD is a common condition that affects women of all ages, characterized by symptoms such as decreased libido, difficulty achieving orgasm, and pain during intercourse. We conducted a comprehensive analysis of changes in core muscles during FSD using machine and deep learning. We evaluated the performance of multiple models, including multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), recurrent neural network (RNN), ElasticNetCV, random forest regressor, SVR, and Bagging regressor. The models were evaluated based on mean squared error (MSE), mean absolute error (MAE), and R-squared (R
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