Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction

Abstract

The first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at reducing leakage episodes by strengthening these muscles. However, many women struggle with performing correct PFM contractions or have misconceptions about their contractions. To address this issue, we present a novel PFM contraction quality assessment system. This system combines a PFM contraction detector with a maximal PFM contraction performance classifier. The contraction detector first identifies whether or not a PFM contraction was performed. Then, the contraction classifier autonomously quantifies the quality of maximal PFM contractions across different features, which are also combined into an overall rating. Both algorithms are based on artificial intelligence (AI) methods. The detector relies on a convolutional neural network, while the contraction classifier uses a custom feature extractor followed by a random forest classifier to predict the strength rating based on the modified Oxford scale. The AI algorithms were trained and tested using datasets measured by vaginal dynamometry, combined in some cases with digital assessment results from expert physiotherapists. The contraction detector was trained on one dataset and then tested on two datasets measured with different dynamometers, achieving 97% accuracy on the first dataset and 100% accuracy on the second. For the contraction performance classifier, the results demonstrate that important clinical features can be extracted automatically with an acceptable error. Furthermore, the contraction classifier is able to predict the strength rating within a ±1 scale point with 97% accuracy. These results demonstrate the system's potential to enhance PFM training and rehabilitation by enabling women to monitor and improve their PFM contractions autonomously.

Keywords: artificial intelligence; pelvic floor muscle contraction; rehabilitation; urinary incontinence; vaginal dynamometer.

Figure 1

Overview of the quality assessment system implemented.

Figure 2

Overview of CNN model architecture and data processing pipeline.

Figure 3

(a) Example of the trapezoid model fitted to a contraction graph. (b) Feature extraction algorithm key aspects.

Figure 4

Example of original data derivative and inverted derivative.

Figure 5

Bar chart of the class distribution of PFM strength recordings from Dataset 1.

Figure 6

Original signal versus reconstructed signal of one PFM contraction from Dataset 1.

Figure 7

Top-three dominant frequencies for (a) original signal versus (b) down-sampled signal for one PFM contraction from Dataset 1.

Figure 8

Examples of PFM contraction signals and their corresponding trapezoid model fit.

Figure 9

(a) Receiver operating characteristic curve for the CNN model. (b) Confusion matrix for CNN model for Dataset 2.