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Convolutional Neural Network in Ovarian Follicle Identification

Assessing a Mask Region-based Convolutional Neural Network in Follicle Identification and Measurement During Ovarian Stimulation

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04545918
Enrollment
80
Registered
2020-09-11
Start date
2021-02-01
Completion date
2021-05-20
Last updated
2021-05-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Infertility, Female, Ovarian Follicular Cyst

Keywords

Infertility, Ovarian Follicle

Brief summary

A prospective cohort trial studying patients with infertility undergoing an ovarian stimulation with exogenous gonadotropins. Ovarian monitoring will be performed with a combination of transvaginal ultrasound and 2 dimensional human measurements of the follicle development on the right and left ovaries along with SonoAVC. Human and SonoAVC measurements will then be compared to mask region-based convolutional neural network in follicle identification and measurement during during the ovarian stimulation.

Detailed 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 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.

Interventions

DEVICEFollicle Clarity

Mask Region-based Convolutional Neural Network in Ultrasound Follicle Identification

Sponsors

Cycle Clarity
Lead SponsorINDUSTRY

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
FEMALE
Age
21 Years to 42 Years

Inclusion criteria

* Infertile women ages 21-42 years old undergoing ovarian hyperstimulation as part of the treatment care. Women with at least one ovary visible with transvaginal ultrasound. Women with at least one follicle 10 mm or greater in average diameter.

Exclusion criteria

* Women less than 21 years of age or greater than 42 years of age. Women without a visible ovary by transvaginal ultrasound. Women without a follicle at least 10 mm in average diameter

Design outcomes

Primary

MeasureTime frameDescription
Median size (mm) of ovarian follicles in CCAI compared to SonoAVC1 monthComparison of the mean size in mm of Cycle Clarity's Artificial Intelligence software (CCAI) compared with GE SonoAVC

Secondary

MeasureTime frameDescription
Number of ovarian follicles in CCAI compared to Sono1 monthComparison of the number of ovarian follicles with Cycle Clarity's Artificial Intelligence software (CCAI) compared with GE SonoAVC

Countries

United States

Contacts

Primary ContactJohn A Schnorr, MD
john.schnorr@cycleclarity.com843-883-6200
Backup ContactSusan R. Schnorr
susan.schnorr@cycleclarity.com843-883-6200

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026