Novel approach for noninvasive pelvic floor muscle strength measurement using extracorporeal surface perineal pressure measurement and machine learning modeling.

Objective: Accurate measurement of pelvic floor muscle (PFM) strength is crucial for the management of pelvic floor disorders. However, the current methods are invasive, uncomfortable, and lack standardization. This study aimed to introduce a novel noninvasive approach for precise PFM strength quantification by leveraging extracorporeal surface perineal pressure (ESPP) measurements and machine learning algorithms. Methods: Twenty-one healthy women participated in this study. ESPP measurements were obtained using a 10 × 10 pressure array sensor during maximal voluntary PFM contractions in a seated position. Simultaneously, transabdominal ultrasound was used to measure bladder base displacement (mm) as a reference for PFM contraction strength. Seven ESPP variables were calculated based on ESPP data and intra- and inter-rater reliabilities were assessed. Machine learning algorithms predicted bladder base displacement from ESPP variables. Results: The ESPP measurements demonstrated good to excellent intra-rater (ICC = 0.881) and inter-rater (ICC = 0.967) reliability. Significant correlations were observed between bladder base displacement and middle ( Conclusion: This study demonstrates the potential of ESPP measurements and machine learning algorithms as a reliable and valid noninvasive approach for assessing PFM strength by quantifying the directionality of contractions, overcoming the limitations of traditional techniques.