Accurate prediction for egg
Patient feedback in IVF cycles
and social egg freezing
Standardized scoring system to facilitate
research in improving egg quality
Available commercially
Non-invasive assessment
Improving success rates per transfer
Decrease twins (single embryo transfer)
Healthier Pregnancy outcomes
Prevent transferring embryos at non-ideal time
Improving success rates per transfer
Decrease twins (single embryo transfer)
Healthier Pregnancy outcomes
Quality control is critical for a successful IVF lab.
Artificial Intelligence will inevitably improve outcomes through automation, objective diagnostics and enhanced decision making – all of which are subject to inherent subjectivity, biases, and limitations when performed by a human.
Doctor + AI = Best Possible Outcome
New tools to help clinicians and embryologists to personalize care and as a result improve outcomes.
Everyone’s bodies are different and your personalized information is critical for your doctor to make the best individualized treatment plan for you. By having the option to get deeper information on not only your embryo but also your eggs, we’re armed with more information to give you options and make the right decisions for your care.
We have developed the first Artificial Intelligence software analysis tool to non-invasively evaluate oocytes (eggs), creating the only objective classification and prediction tool for an oocyte.
Insights yielded by our patent-pending technology will be valuable to patients, clinicians, and researchers, and have numerous clinical applications including an enhanced non-invasive assessment tool for embryo quality and endometrial receptivity.
We are seeking strategic partners to help us in our mission to build advanced diagnostic and predictive tools for reproductive medicine.
MD, MSC, Researcher at Clinical for Clinical Effectiveness and Health Policy (Argentina)
Associate Professor, Reproductive Endocrinology and Infertility UCSF
Assistant Research Professor, Information Technology.
Computer Vision and knowledge based systems
Johns Hopkins
Television Host
CTV, eTalk