Department of Physical Therapy for Women's Health, Faculty of Physiotherapy, Deraya University, EL-Minia, Egypt.
Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt.
Sci Rep. 2024 Feb 27;14(1):4795. doi: 10.1038/s41598-024-54967-0.
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) 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 R score (0.988), while random forest regressor also performed well with an MSE of 0.0021 and an R 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.
本研究旨在探讨核心肌群在女性性功能障碍（FSD）中的作用，并制定综合康复方案来解决这一问题。我们旨在回答以下研究问题：核心肌群在 FSD 中的作用是什么，以及机器和深度学习模型如何准确预测 FSD 期间核心肌群的变化？FSD 是一种常见病症，影响所有年龄段的女性，其特征是性欲减退、难以达到性高潮以及性交时疼痛等症状。我们使用机器和深度学习对 FSD 期间核心肌群的变化进行了全面分析。我们评估了多个模型的性能，包括多层感知器（MLP）、长短期记忆（LSTM）、卷积神经网络（CNN）、递归神经网络（RNN）、弹性网络 CV、随机森林回归器、支持向量回归（SVR）和袋装回归器。模型基于均方误差（MSE）、平均绝对误差（MAE）和 R 平方（R）得分进行评估。我们的结果表明，CNN 和随机森林回归器是预测 FSD 期间核心肌群变化最准确的模型。CNN 的 MSE 最低（0.002），R 分数最高（0.988），而随机森林回归器的 MSE 为 0.0021，R 分数为 0.9905，表现也很好。我们的研究表明，机器和深度学习模型可以准确预测 FSD 期间核心肌群的变化。被忽视的核心肌群在 FSD 中起着重要作用，这突出了需要制定综合康复方案来解决这些肌肉的问题。通过制定这些方案，我们可以提高 FSD 女性的生活质量，帮助她们实现最佳的性健康。
