Retrospective results show that more than half of IVF cycles had possible early or late trigger injections, which affected the outcomes of egg recovery.
SAN FRANCISCO, 16 May 2022 / PRNewswire / – A study led by researchers at Alife HealthA fertility technology company building artificial intelligence (AI) tools designed to improve in vitro fertilization (IVF) outcomes has found that an interpretable machine learning model can help doctors optimize ovulation, accelerate injection time to achieve patient outcomes for a significant improve number of patients.
When undergoing IVF, patients are prescribed fertility medication to stimulate the ovaries to produce multiple eggs or oocytes. In this process, physicians make a series of decisions that are critical to the outcome of the cycle. One of the most important decisions is when to give the final trigger injection to cause maturation of the oocytes. Activating prematurely may not allow the oocytes to reach maturity, whereas activating too late can lead to post-adult oocytes – both reducing the chances of successful fertilization and the creation of healthy embryos to use during IVF pregnancy. .
The study, published online in Fertility and Sterility, is one of the first to develop an interpretable machine learning model to help clinicians optimize the day of trigger during ovarian stimulation. For their analysis, conducted with collaborators at RMA New York, Boston IVF, RSC Bay Area and UCSF, the researchers drew from more than 30,000 historical IVF cycles performed during 2014 and 2020 at various centers.
Study results suggest that Alife’s machine learning model may help physicians harvest an average of two to three more mature oocytes (eggs), two more fertilized oocytes (sperm-fertilized eggs) and another useful blastocyst (embryo). The findings not only confirm previously reported results, but do so across several different clinics and with a much larger sample size. The authors note that the study has limitations, the primary of which is its retrospective nature.
“Our results suggest that significant improvements in outcomes can potentially be achieved for a large percentage of ovarian stimulation cycles by using this model to help with trigger injection timing,” says the study’s senior author. Kevin Loewke, head of data science at Alife. “We look forward to entering the clinic and conducting prospective studies in the near future to confirm these retrospective findings.”
“These promising results further indicate that we are on track to use AI to improve the efficacy of IVF for our patients,” said the study’s co-author, Edward Hariton, MD, MBA. “As we aim to leverage technology to not only improve the outcomes for our patients, but also increase the efficiency of our providers and expand access to care, clinical decision support tools such as these will be crucial.”
The study, entitled “An Interpretable Machine Learning Model for Predicting the Optimal Day of Trigger During Ovarian Stimulation,” was led by Michael FantonPhD, senior data scientist at Alife and co-author by:
- Paxton Maeder-York, MS, MBA, CEO of Alife Health
- Edward HaritonMD, MBA, Reproductive Endocrinology and Fertility Fellow at UCSF Center for Reproductive Health, joining RSC Bay Area later this year
- Oleksii BarashPhD, HCLD, IVF Laboratory Director at RSC Bay Area
- Louis WecksteinMD, Reproductive Endocrinologist at RSC Bay Area
- Denny SakkasPhD, CSO from Boston IVF
- Alan CoppermanMD, FACOG, Reproductive Endocrinologist at RMA New York
- Kevin LoewkePhD, Head of Computer Science at Alife Health
ABOUT LIFE HEALTH
Alife’s mission is to modernize and personalize the IVF process with the latest artificial intelligence technology to improve outcomes and care for all. The company has built a consortium of partnerships with the top clinics and most renowned physicians to bring significant clinical improvements to patients worldwide. The company, founded in 2020 by Paxton Maeder-York, is based in San Francisco and backed by top-tier venture capitalists, including Lux Capital, Union Square Ventures and Maveron. To learn more, visit www.alifehealth.com.
Jamie Gray, [email protected]
SOURCE Alife Health