Prevalence of sexual dysfunction in women with cervical cancer: a systematic review and meta-analysis

Qian, Maolin; Wang, Lan; Xing, Jiajun; Shan, Xiao; Wu, Juan; Liu, Xiaoqin

doi:10.1080/13548506.2022.2110270

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…FSD may manifest in diverse ways, such as a diminished desire for sexual activity, difficulties in attaining or sustaining sexual arousal, challenges reaching orgasm, discomfort or pain during intercourse, and other associated symptoms 2 . FSD is not a rare condition, with prevalence ranging from 38 to 85.2% 3 , 4 .…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing core muscle analysis in female sexual dysfunction based on machine learning

Abdel Hady,

Abd El-Hafeez

2024

Sci Rep

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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 (R2) score. Our results show that CNN and random forest regressor are the most accurate models for predicting changes in core muscles during FSD. CNN achieved the lowest MSE (0.002) and the highest R2 score (0.988), while random forest regressor also performed well with an MSE of 0.0021 and an R2 score of 0.9905. Our study demonstrates that machine and deep learning models can accurately predict changes in core muscles during FSD. The neglected core muscles play a significant role in FSD, highlighting the need for comprehensive rehabilitation programs that address these muscles. By developing these programs, we can improve the quality of life for women with FSD and help them achieve optimal sexual health.

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Section: Introductionmentioning

confidence: 99%