This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.
The use of machine learning techniques using an artificial intelligence tool is proposed to analyze clinical data to predict best possible IVF/ART outcomes. This tool has been utilized to accurately predict embryo quality here at Cornell. Utilizing this tool to assess objective clinical findings and predict outcomes of assisted reproductive techniques is sought, with the ultimate goal of an automated tool to reduce implicit physician bias. Within this goal, using this tool to objectively and accurately assess baseline ovarian reserve at the start of an ART cycle is proposed, using 3D sonography to image the ovary and artificial intelligence tool to objectively identify baseline antral follicle counts.
The Use of Artificial Intelligence for Clinical Assessment of Assisted Reproductive Techniques and IVF Outcomes
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
| Inclusion Criteria | Exclusion Criteria |
|---|---|
|
|
Sponsor: Weill Medical College of Cornell University
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.