Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment.

This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, a topic of considerable interest in the medical and aesthetic fields. We explore the potential of cavitation to alter abdominal fat composition and delve into the optimization of fat prediction models using advanced hyperparameter optimization techniques, Hyperopt and Optuna. Our objective is to enhance the predictive accuracy of abdominal fat dynamics post-cavitation treatment. Employing a robust dataset with abdominal fat measurements and cavitation treatment parameters, we evaluate the efficacy of our approach through regression analysis. The performance of Hyperopt and Optuna regression models is assessed using metrics such as mean squared error, mean absolute error, and R-squared score. Our results reveal that both models exhibit strong predictive capabilities, with R-squared scores reaching 94.12% and 94.11% for post-treatment visceral fat, and 71.15% and 70.48% for post-treatment subcutaneous fat predictions, respectively. Additionally, we investigate feature selection techniques to pinpoint critical predictors within the fat prediction models. Techniques including F-value selection, mutual information, recursive feature elimination with logistic regression and random forests, variance thresholding, and feature importance evaluation are utilized. The analysis identifies key features such as BMI, waist circumference, and pretreatment fat levels as significant predictors of post-treatment fat outcomes. Our findings underscore the effectiveness of hyperparameter optimization in refining fat prediction models and offer valuable insights for the advancement of non-invasive fat reduction methods. This research holds important implications for both the scientific community and clinical practitioners, paving the way for improved treatment strategies in the realm of body contouring.

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.