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.

Study Overview

• Status: Completed
• Conditions: Female Sexual Dysfunction
• Intervention / Treatment: Other: no intervention

• Study Type: Observational
• Enrollment (Actual): 100

Contacts and Locations

Study Locations

• Egypt
  • Minia
    • Minya, Minia, Egypt
      • Deraya university

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Probability Sample

Study Population

100 patients were distributed randomly into two groups. Female sexual dysfunction group and another group is normal females. Two group will be measure Transverses muscle ratio, Multifidus muscle ratio ,diaphragm excursion and force of contraction of pelvic floor muscles by ultrasound imaging, VLQ and FSFI questionnaires for female sexual function VLQ and FSFI questionnaires for female sexual dysfunction and normal females. Patients will be examined by radiologist with medical ultrasound imaging

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Female sexual dysfunction group	Other: no intervention; no intervention
Normal females	Other: no intervention; no intervention

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diaphragm excursion	Ultrasound imaging curvilinear transducer	2 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Force of contraction of pelvic floor muscles	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	2 months

Collaborators and Investigators

• Sponsor: Deraya University

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2023
• Primary Completion (Actual): April 10, 2023
• Study Completion (Actual): April 15, 2023

Study Registration Dates

• First Submitted: April 16, 2023
• First Submitted That Met QC Criteria: April 16, 2023
• First Posted (Actual): April 27, 2023

Study Record Updates

• Last Update Posted (Actual): September 26, 2023
• Last Update Submitted That Met QC Criteria: September 24, 2023
• Last Verified: September 1, 2023

More Information

Terms related to this study

• Keywords: Core Muscles, machine learning,female sexual dysfunction, KNN
• Other Study ID Numbers: P.REC/ 6/2023

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No