Convolutional Neural Network in Ovarian Follicle Identification
Last updated on July 2021Recruitment
- Recruitment Status
- Recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Infertility Female
- Ovarian Follicular Cyst
- Type
- Observational
- Design
- Observational Model: CohortTime Perspective: Prospective
Participation Requirements
- Age
- Between 21 years and 42 years
- Gender
- Only males
Description
This is a prospective cohort trial in which a total of 80 female subjects with infertility between the ages of 21 and 42 years of age undergoing ovarian stimulation will be recruited. After giving informed written consent the subject to undergo standard ovarian ultrasound monitoring with transvagina...
This is a prospective cohort trial in which a total of 80 female subjects with infertility between the ages of 21 and 42 years of age undergoing ovarian stimulation will be recruited. After giving informed written consent the subject to undergo standard ovarian ultrasound monitoring with transvaginal ultrasounds during the ovarian stimulation. Monitoring will be performed with two-dimensional measurements of each follicle greater than 10 mm in size by the ultrasonographer. SonoAVC will then be applied to both ovaries for automated counting and measurement of the follicles within the ovaries. The patient will then undergo two 6-second ultrasounds of the right and left ovaries which will then be transmitted in a DICOM format to mask regional based recurrent neural network which is been trained and validated for follicle detection and quantification using curated transvaginal ultrasound images.
Tracking Information
- NCT #
- NCT04545918
- Collaborators
- Not Provided
- Investigators
- Study Chair: John A. Schnorr, MD Cycle Clarity, Founder