Fertility Disorders
Conditions
Brief summary
This protocol supports the reliable development and improvement of Artificial Intelligence (AI)-based applications by enabling training, verification, and validation using high-quality real clinical data. It also aims to enhance machine learning in developmental-use AI software. In addition, the professional user simulated use will ensure correct and easy use of the AI applications.
Detailed description
This data collection protocol aims to allow the reliable and robust development (training, verification, and validations) of Artificial Intelligence (AI) technology-based applications as well as improving the machine learning stage of released devices/applications - an AI - based software, for developmental use only, through inspection of large body of high quality real clinical data. Another important element in the design of the application is the user interface. It is thus important to run simulated use assessments to ensure correct and easy use of the AI application by professional users.
Interventions
DEVICEAI evaluation of embryos
AI evaluation of embryos inside a time-lapse incubator.
Sponsors
Fairtility
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Images and data of eggs or embryos available from egg retrievals by women undergoing standard egg-freezing or In vitro fertilization (IVF) treatments, using their own eggs or donor eggs. 2. Women at least 18 years of age. 3. Embryos or eggs cultured in a time-lapse incubator connected to the AI Embryo Viewer.
Exclusion criteria
* Women with no available own or donor eggs.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Area under the curve (AUC) will be quantified using logistic regression to assess EQ Score's prediction of utilization. | 7 days |
Countries
United States
Contacts
Primary ContactPedro Pini, M.D
Outcome results
None listed