The Use of Artificial Intelligence for Clinical Assessment of Assisted Reproductive Techniques and IVF Outcomes
Weill Medical College of Cornell University
Summary
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.
Description
This study will collect prospective data, specifically 3D transvaginal ultrasound of ovaries at time of baseline evaluation at beginning of an ART cycle. All participants will be asked to give written consent to be included in the study. At the time of initial ultrasound that is routinely done on the first day of the ART cycle, the physician performing the ultrasound will use a 3D ultrasound transvaginal probe to perform the ultrasound and capture both 2D and 3D images. 3D ultrasound is performed routinely for patients undergoing ART and is not an investigative procedure, however is not unifor…
Eligibility
- Age range
- 18–89 years
- Sex
- All
- Healthy volunteers
- Yes
Inclusion Criteria: * All patients undergoing ovarian stimulation (including OI and IVF cycles) * Treatment for fresh embryo transfer and cryopreservation of oocytes or embryos upfront * Healthy male partners of the female subjects who agree to be part of the study. Exclusion Criteria: * None
Interventions
- OtherAI to analyze 3 D ultrasound
AI to assess 3 D ultrasound to assess antral follicle count
Location
- Weill Cornell MedicineNew York, New York