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OpenTrials
Completed

NCT Number: NCT05833685

Predicting Changes in Core Muscles During Female Sexual Dysfunction: A Comprehensive Analysis Using Machine and Deep Learning

The purpose of this study is to Predicting changes in core muscles during female sexual dysfunction by A Comprehensive Analysis Using Machine and Deep Learning Female sexual dysfunction (FSD) is a common condition that affects womenof all ages. It is characterized by a range of symptoms, including decreased libido, difficulty achieving orgasm, and pain during intercourse. One potential cause of FSD is muscular weakness or changes in the core muscles. These muscles play an important role in sexual function, and changes in their strength or activation patterns can lead to FSD. Additionally, the development of a machine learning model for this purpose could pave the way for future studies exploring the use of artificial intelligence in the diagnosis and treatment of other musculoskeletal disorder and female health issues.

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Key information

Conditions

Female Sexual Dysfunction

Age range

30 year–40 year

Sex eligibility

Female

Study type

Observational

Primary location

Deraya university

Minya, Minya Governorate, Egypt

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • a number of parities ≤ three
  • normal vaginal deliveries

Exclusion criteria

  • History of a recto-vaginal or vesico-vaginal fistula, undiagnosed uterine bleeding urinary tract infection,
  • diabetes,
  • intrauterine device
  • sexual disorder

Treatment and study plan

No intervention

Other

no intervention

Primary outcomes

  1. Diaphragm excursion

    Time frame: 2 months

    Ultrasound imaging curvilinear transducer

Secondary outcomes

  1. Force of contraction of pelvic floor muscles

    Time frame: 2 months

    ultrasound imaging, convex transducer was used at a frequency of 5 MHz for evaluating. Voluntary PFM contractions' force (strength) of all patients. It has a good inter-rater reliability for measuring PFM force (ICC, 0.81, 0.7123) respectively, as well as a good intra-rater reliability (ICC,0.98, 0.9841) respectively

Sponsors and collaborators

Lead sponsor

Deraya University

Other

Registry information

Important dates

Study start
2023
Primary completion
2023
Study completion
2023
First posted
Apr 27, 2023
Registry last updated
Sep 26, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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