Artificial Intelligence (also known as AI) encompasses a broad range of technologies that enable the computer or "machine" to think and be intelligent. Some examples of AI are self-driving cars and Amazon's Alexa. Machine learning is a specific class of techniques or methodologies under the broader umbrella of artificial intelligence. 

Machine learning refers to the use of advanced data mining, statistics and computer science to use past patterns to predict a future outcome in a way that is validated (i.e. proven) and reproducible.  Techniques used to apply machine learning have been utilized by experts for several decades but their application to solving real-world problems have become feasible with advances in cloud computing.  

Machine learning is an established, powerful way to tackle complex data sets encountered in the IVF space. We're already using machine learning in our everyday lives: It runs most internet search engines, recommendations from Amazon and Netflix, and the "people you may know" feature on Facebook or Linkedin. In other words, we are afforded more accuracy and personalization in our online shopping experiences than most patients receive when making emotional, life-affirming and costly medical decisions such as moving forward with IVF treatment to have a family.

How does Univfy® apply machine learning and artificial intelligence for IVF?

The Univfy AI Platform has learned mathematical relationships relating clinical factors and IVF outcomes from possibly the most diverse IVF database comprising data of 250,000+ IVF cycles and 500,000+ embryos from IVF centers in the U.S., Canada, Europe and Asia.

This learning by "the machine" has enabled Univfy to develop and validate accurate, center-specific IVF success prediction models for fertility centers ranging from very small centers performing 100-200 cycles per year to large academic centers performing thousands of IVF cycles per year.

The proprietary application of machine learning to IVF allows Univfy to communicate personalized predictions of IVF treatment outcomes to patients via the Univfy PreIVF Report. The types of clinical predictors (or variables) aren’t new, but only recently with machine learning and cloud computing technologies have we been able to use it to help fertility patients in an impactful way.

While age is a significant predictor of a woman’s chances of having a baby from IVF, we have found that it consistently contributes to only 50% of the prediction. That means it is important to use other predictors which make up the other 50% of the prediction, even though each of those predictors on its own may contribute to only 5 to 10% of the prediction.

Markers of ovarian reserve, embryo quality measures, reproductive history, and health data such as body mass index (BMI) and chronological age have all been individually associated with fertility treatment outcomes. However, unless these factors are built into a prediction model that can be objectively and quantitatively tested, the information they carry is not easily and accurately accessed because there is a tremendous amount of correlation and redundancy of information among these predictors.

How do we combine our machine learning and AI capabilities with fintech to create solutions for patients and physicians?

Fintech is using technology to innovate the way customers, or patients, pay for and afford treatment, services or products.

In the case of Univfy, we help providers develop financial packages using accurate, data-driven predictions about a patient’s likelihood of success. Predictions made through machine learning is the reason why more than 80 percent of patients can qualify for a refund program, while traditional methods can only qualify very few.

About the Author:

Mylene Yao, M.D. | Co-Founder and CEO of Univfy®

Dr. Mylene Yao is a board-certified OB/GYN with more than 20 years of experience in clinical and reproductive medicine research. Prior to founding Univfy, she was on the faculty at Stanford University, where she led NIH-funded fertility and embryo genetics research and developed The Univfy AI Platform with the academic founding team. See her full bio here.