About Univfy®
The Univfy® AI Platform makes fertility costs and success more affordable and predictable for women and couples navigating their family-building options. Univfy empowers individuals with personalized information and counseling to inform smarter spending and decision-making, maximizing their chances of success, while increasing growth and efficiency for providers.
Univfy is the only highly-scalable AI platform to provide scientifically-validated, personalized reports that counsel patients from diverse demographics about their probability of having a baby with IVF. The Univfy® PreIVF Report provides individualized prognostics, showing each patient her own probability of having a baby with up to three IVF cycles. The probabilities are based on the woman or couple’s holistic health profile, including their age, BMI, reproductive history, ovarian reserve test results and their partner’s semen analysis. The predictions are validated based on the clinical IVF outcomes data of the center where the patient is receiving treatment. Univfy’s technology has shown that more than 50% of patients have IVF success probabilities that are higher than estimated by age.
Through technology developed by Stanford University researchers, Univfy uses a rigorous scientific process to develop and validate prediction models and to provide essential information to support patient counselling. Our methods have been published in top, peer-reviewed research publications, in which we reported and established benchmarks for measuring the performance of our IVF outcomes predictive model. Univfy has a global IP portfolio, comprising patents issued in the US and other countries. See below for a timeline of Univfy’s research, partnerships and commercialization.
Univfy Research & Timeline
Our Published Research: Validated IVF Success Prediction Models & Clinical Outcomes
Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model
Yao MWM§, Nguyen ET§, Retzloff MG, Gago LA, Nichols JE, Payne JF, Ripps BA, Opsahl M, Jeremy Groll, Beesley R, Neal G, Adams J, Nowak L, Swanson T, Chen X. Nature Communications, 2025, 16:3661. https://doi.org/10.1038/s41467-025-58744-z
Yao MWM, Nguyen ET, Retzloff MG, Gago LA, Copland S, Nichols JE, Payne JF, Opsahl M, Cadesky K, Meriano J, Donesky BW, Bird III J, Peavey M, Beesley R, Neal G, Bird, Jr. JS, Swanson T, Chen X, Walmer DK. J Clin Med 2024, 13(12),3560: https://doi.org/10.3390/jcm13123560
Jenkins J, van der Poel S, Krussel J, Bosch E, Nelson SM, Pinborg A, Yao MWM. Reproductive Biomedicine Online 2020. doi:10.1016/j.rbmo.2020.07.005
Nelson SM, Fleming R, Gaudoin M, Choi B, Santo-Domingo K, Yao MWM. Fertil Steril 2015. doi:10.1016/j.fertn-stert.2015.04.032 §Co-first authors.
Personalized prediction of first-cycle in vitro fertilization.
Choi B, Bosch E, Lannon BM, Leveille MD, Wong WH, Leader A, Pellicer A, Penzias AS, Yao MWM. Fertil Steril 2013;99(7):1905-11. doi: 10.1016/j.fertnstert.2013.02.016. Epub 2013 Mar 21.
Predicting personalized multiple birth risks after in vitro fertilization-double embryo transfer.
Lannon BM, Choi B, Hacker MR, Dodge LE, Malizia BA, Barrett CB, , Wong WH, Yao MWM, Penzias AS. Fertil Steril 2012;98(1):69-76. doi:10.1016/j.fertnstert.2012.0411. Epub 2012 Jun 4.
Deep phenotyping to predict live birth outcomes in in vitro fertilization.
Banerjee P§, Choi B§, Shahine LK, Jun SH, O’Leary K, Lathi RB, Westphal LM, Wong WH, Yao MWM. PNA 2010;107(31):13559-60. doi: 10.1073/pnas.1002296107. Epub 2010 Jul 19.
Defining human embryo phenotypes by cohort-specific prognostic factors.
Jun SH§, Choi B§, Shahine L, Westphal LM, Behr B, Reijo Pera, Wong WH, Yao MWM. PLoS ONE 2008; 3(7):e2562. doi: 10.1371/journal.pone.002562.
Read our blog posts for a deep dive into how Univfy’s AI/machine learning technology works:
About Univfy® Founders
Dr. Mylene Yao, Cofounder and CEO, has led Univfy from technology invention and platform development to commercialization. Dr. Yao’s vision is to combine healthcare AI, scientific validation and fintech to power women’s and couple’s access to the most effective and safest fertility treatments. She has more than 20 years of experience in clinical and scientific research in reproductive medicine. Prior to Univfy, Dr. Yao was faculty at Stanford University where she led NIH-funded fertility and embryo genetics research and developed the Univfy technology with the academic founding team.
Dr. Yao graduated from University of Toronto Medical School and completed her OB/GYN residency training at McGill University. She received her clinical subspecialty training in Reproductive Endocrinology and Infertility at Brigham and Women’s Hospital at Harvard University. Dr. Yao received awards for her research on pre-implantation embryo development and uterine receptivity. She is co-author of the Infertility chapter in Berek and Novak’s Gynecology, the top medical textbook for OB/GYNs.
Dr. Yao grew up in Toronto and has made US her home for more than twenty years. She currently lives in the San Francisco Bay Area with her husband and two children.
Professor Wing H. Wong, Cofounder and Scientific Advisor, is the Stephen R. Pierce Family Goldman Sachs Professor in Science and Human Health and Professor in the Departments of Statistics (former chairman) and Biomedical Data Science at Stanford University. Professor Wong and his team began research collaborations with Dr. Yao during the academic research phase of Univfy’s technology development. Professor Wong has provided critical scientific advisory on many fronts, including establishing objective metrics for evaluating prediction model performance and the testing of machine learning parameters required specifically for IVF prediction models. Professor Wong is also a co-inventor on a number of Univfy patent applications.
Professor Wong’s seminal contributions to theoretical statistics – most notably, Bayesian network structures, machine learning, Monte Carlo and applied statistics in computational biology – have been recognized with numerous awards and honors. In 2009, he was elected to the US National Academy of Sciences for developing innovative high-throughput genomics analysis tools, pivotal in driving genomics research in the past decade.
