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.
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.
None
Single group
AI to analyze 3 D ultrasound
Other interventionAI to assess 3 D ultrasound to assess antral follicle count
Number of baseline antral follicle count
To assess the accuracy and feasibility of using our artificial intelligence tool to assess antral follicle count, an indicator of baseline ovarian reserve, at time of baseline ultrasound for ART compared to qAVCantral and manual follicle counts to number of total oocytes retrieved.
Number of retrieved oocytes
To assess the accuracy and feasibility of using our artificial intelligence tool
Number of mature oocytes
Number of mature oocytes (ART cycles)
Number of multiple gestation
Number of multiple gestation for OI cycles.
Number of clinical intrauterine pregnancies IVF
Clinical intrauterine pregnancies
Number of clinical intrauterine pregnancies OI
Clinical intrauterine pregnancies
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