Ultrasound imaging of core muscles activity in multiparous women with vaginal laxity: a cross-sectional study

Vaginal laxity (VL) is a common condition among multiparous women, especially those who have delivered vaginally. Since pelvic floor muscles (PFMs) work synergistically with other core muscles, physical therapy protocols that aim to treat VL should train the PFMs in combination with other core muscles. To investigate the activity of core muscles in multiparous women with and without VL, and its relation to sexual function. An observational, cross-sectional study. The study included 100 multiparous women, who were divided into two groups according to their scores on the vaginal laxity questionnaire (VLQ). Women who scored between 1 and 3 on the VLQ were categorized as having VL (n = 48), while those who scored between 5 and 7 were placed in the control group (n = 52). The primary outcomes were PFM displacement, diaphragmatic excursion, transversus abdominis activation ratio, and lumbar multifidus thickness measured by ultrasound imaging. The secondary outcome was sexual functioning, evaluated using the Arabic female sexual function index (ArFSFI). The VL group had significantly lower PFM displacement (mean difference (MD) - 0.42; 95% confidence interval (CI) - 0.49 to - 0.33; p = 0.001), diaphragmatic excursion (MD - 2.75; 95% CI - 2.95 to - 2.55; p = 0.001), lumbar multifidus thickness (MD - 10.08; 95% CI - 14.32 to - 5.82; p = 0.02), and ArFSFI scores (MD - 9.2; 95% CI - 10.59 to - 7.81; p = 0.001) in comparison to the control group (p < 0.05). Nevertheless, the transversus abdominis activation ratio demonstrated no significant difference between the two groups (MD 0.06; 95% CI - 0.05 to 0.17; p = 0.33). Multiparous women with VL had significantly lower PFM displacement, diaphragmatic excursion, lumbar multifidus thickness, and sexual function index scores than women in the control group. The only exception was transversus abdominis activation, which did not differ significantly between the VL and control groups.

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

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.

Effect of knack technique on vaginal laxity in multiparous women: A randomized controlled trial

BACKGROUND

Vaginal laxity defined according to the International Urogynecological Association/International Continence society is excessive vaginal looseness; it is one of the most common symptoms of pelvic floor dysfunction, which refers to medical/functional disorders that can have a significant impact on a woman's sexual self-esteem and sexual life.

AIM

This study aimed to determine the impact of the Knack Technique on Pelvic floor muscle (PFM) and sexual function in women with vaginal laxity.

METHODS

Thirty females complaining of vaginal laxity were randomly chosen from the outpatient clinic at Deraya University. Their ages varied from 35 to 45 years, their body mass index was 25-30 kg/m2, and a number of parities ≤ three normal vaginal deliveries and at least two years from the last delivery complaining about vaginal laxity, water entrapment, and loss of friction during sexual intercourse. They were randomized into two equal groups at random (A, B). Group A (15 females) received (PSTES) and group B (15 females) received (PSTES) and Knack Technique. Both the groups received three sessions per week for 2 months.

EVALUATION

The outcome measures were evaluated through pre- and post-interventions by using ultrasonography imaging to assess PFM function, Sexual Satisfaction Index, and Vaginal Laxity Questionnaires (VLQ) to assess sexual function.

RESULTS

Analysis indicated a significant improvement in vaginal laxity in the two groups. Comparison between groups pre- and posttreatment showed that there was no statistically significant difference between the groups B and A in SSI and VLQ, while there were significant differences between the groups A and B in PFM force.

CONCLUSION

Combining Parasacral transcutaneous electrical stimulation (PSTES) and Knack Technique is more effective than PSTES alone in reducing vaginal laxity as well as improving PFM and sexual function in women with vaginal laxity.

Predicting female pelvic tilt and lumbar angle using machine learning in case of urinary incontinence and sexual dysfunction

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 R2. Pelvic tilt prediction achieved R2 values > 0.9, with AdaBoost (R2 = 0.944) performing best. Lumbar angle prediction performed slightly lower with decision tree achieving the highest R2 of 0.976. Developing a machine learning model to predict pelvic tilt and lumbar angle has the potential to revolutionize the assessment and management of this condition, providing faster, more accurate, and more objective assessments than traditional methods.