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Related Concept Videos

Muscles of the Pelvic Floor and Perineum01:26

Muscles of the Pelvic Floor and Perineum

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The muscles of the pelvic floor and perineum are crucial for supporting the pelvic organs, controlling continence, and aiding in sexual function, childbirth, and core stability. They are typically divided into the superficial perineal layer and the deep pelvic floor layer.
Perineal Layer
The perineum is a diamond-shaped area below the pelvic diaphragm, divided into an anterior urogenital triangle that contains the external genitals and a posterior anal triangle housing the anus. The urogenital...
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AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction

Juan Chen1, Jiahui Yao2, Wei Chen3

  • 1Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College; National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, China.

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Summary

An AI-powered diagnostic model significantly improves pelvic floor dysfunction (PFD) diagnosis by establishing new AI-Reference ranges for surface electromyography (sEMG) parameters, outperforming the older Glazer protocol.

Keywords:
Artificial intelligencePelvic floor dysfunctionsPelvic floor muscleReference rangesSurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Pelvic Floor Disorders Research

Background:

  • Pelvic floor surface electromyography (sEMG) is crucial for evaluating and treating pelvic floor dysfunctions (PFDs).
  • The existing Glazer protocol, developed over 20 years ago with a limited sample size, faces challenges in accurately diagnosing PFDs across diverse populations.
  • There is a need for updated, more reliable reference ranges and advanced diagnostic methods for PFDs.

Purpose of the Study:

  • To establish a multidimensional database for monitoring pelvic floor sEMG.
  • To derive more accurate AI-Reference ranges for sEMG parameters.
  • To develop and validate an artificial intelligence (AI) model for the accurate diagnosis of PFDs.

Main Methods:

  • A population-based, multicenter, cross-sectional study involving 1605 participants from 21 centers across China.
  • Development of a multidimensional sEMG database and an AI-Diagnostician-PFD diagnostic model using AI.
  • Data splitting into training (60%), testing (40%), and independent validation datasets, ensuring representation across diverse geographical regions.

Main Results:

  • The AI-Reference ranges demonstrated an 11% higher area under the receiver operating characteristic curve (AUC) than the Glazer standard in the external validation dataset.
  • The AI-Diagnostician-PFD model achieved AUCs of 0.81 (internal) and 0.79 (external validation), surpassing the Glazer standard's AUCs of 0.76 and 0.68.
  • The AI model exhibited superior diagnostic performance for PFDs, with a 1% higher AUC compared to other classical and deep learning models.

Conclusions:

  • AI-derived reference intervals significantly outperform the established Glazer standard for PFD diagnosis.
  • The AI-Diagnostician-PFD model offers enhanced accuracy and will be made freely available as software.
  • Implementation of this AI algorithm in clinical practice is expected to improve individual PFD diagnosis and population health outcomes.