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

Deraya University100 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

100

Conditions

Female Sexual Dysfunction

Interventions

no interventionOTHER

no intervention

Eligibility

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

Diaphragm excursion

Ultrasound imaging curvilinear transducer

Time frame: 2 months

Secondary Outcomes

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