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

Abstract

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.

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

“…Hady et al 1 conducted a study addressing urinary incontinence (UI) and its correlation with pelvic floor dysfunction (FSD) in women. UI, characterized by uncontrolled urine leakage, is linked to pelvic floor muscle (PFM) activity, impacting trunk and lumbo-pelvic stability.…”

“…Female sexual dysfunction (FSD) encompasses a spectrum of sexual health challenges that can influence women across various age groups 1 . This condition holds the potential to profoundly affect a woman's overall quality of life, interpersonal relationships, and self-esteem.…”

Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital

“…A study published in [3] investigated the use of machine learning to predict pelvic tilt and lumbar angle in women experiencing urinary incontinence. AdaBoost exhibited high accuracy (R 2 � 0.944) for pelvic tilt prediction, suggesting potential advancements in assessing pelvic foor dysfunction.…”

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification

“…The latest advances in computer vision and artificial intelligence have profoundly impacted the development of diagnostic systems for face recognition [ 6 ], disease diagnosis [ 7 – 10 ], etc. Important image processing methods are the backbone of computer vision applications [ 11 – 14 ]. Imprecise diagnosis of Diabetic Retinopathy (DR), a more severe form of Diabetic Eye Disease (DED), may lead to significant blindness.…”