The purpose of this study is to Predicting female pelvic tilt and lumbar angle using machine learning in case of urinary incontinence and sexual dysfunction

92 patients were distributed randomly into two groups. The first group will be measured pelvic tilt, lumbar angle by spinal mouse and force of contraction of pelvic floor muscles by ultrasound imaging, UDI-6 and FSFI questionnaires for urinary incontinence female The second group will be measured pelvic tilt, lumbar angle by spinal mouse device and force of contraction of pelvic floor muscles by ultrasound imaging, UDI-6 and FSFI questionnaires for normal females.

Patients will be examined by radiologist with medical ultrasound imaging and by me with spinal mouse device.

Inclusion Criteria:

• - Their ages varied from 30to 40years, their body mass index (BMI) was 25-30 kg/m2 and a number of parities ≤ three normal vaginal deliveries, at least two years and Subjects hadn't received drugs, patients with mild and moderate urinary incontinence with sexual dysfunction

Exclusion Criteria:

- 1. History of spinal surgery or spinal fracture. 2. Volunteers with a history of a recto-vaginal or vesico-vaginal fistula, undiagnosed uterine bleeding urinary tract infection, diabetes, intrauterine device, chest and/or cardiac disease and using any drugs for urinary incontinence 3. Serious diseases, such as heart disease, kidney, liver diseases, gastric ulcer or duodenal ulcer.

4. A history of bronchial asthma or any chest disease. 5. Uncontrolled diabetes or hypertension. 6. Patients with peacemaker or any metal implant on the treated area. 7. Cancer or patient with past history of tumor excision.

Their ages varied from 30to 40years, their body mass index (BMI) was 25-30 kg/m2 and a number of parities ≤ three normal vaginal deliveries