What Role Can AI Have in IVF?

In vitro fertilization (IVF) isn’t a single medical procedure to help with infertility but a complex series of procedures. Along the way, clinicians, lab technicians, and embryologists are tasked with making many decisions to improve your chance of success. While there’s a tremendous amount of data available that clinicians could use to make those decisions, there’s so much data that it can be incredibly challenging to sort through and extract actionable insights. Artificial intelligence (AI) can distill large quantities of data into simple, easy-to-use tools that help fertility specialists make faster and more data-driven decisions.

6 Second Snapshot

  • Though the technologies around IVF are constantly improving, only about a third of patients have a successful live birth.

  • AI technology has the potential to optimize IVF processes and help doctors make more data-driven decisions.

  • By aggregating and interpreting data, AI aims to help to improve fertility treatment outcomes, reduce costs, and make the entire IVF process more transparent.

First of All, What Is Artificial Intelligence?

The term “artificial intelligence” may remind you of bumbling robots like C3PO or the simulated reality created in The Matrix. However, in practice, AI can be much simpler than that.

AI refers to systems or machines that are programmed to “think” and “work” like a human would. The reason that AI is becoming so popular is that it can process vastly more information than a human could in a shorter amount of time, remain objective during the process, and even find patterns in the data that may not be obvious to a human. In the case of improving fertility outcomes, AI can be an indispensable tool toward processing data and making it easy to use.

Importantly, we view AI as a tool to help augment human intelligence, and not replace it. So while a clinician may use AI to assist in their decision-making, it is ultimately the clinician making the call.

How Might AI Make a Difference in IVF?

Though the technologies around IVF treatment are constantly improving, only about a third of patients have a successful live birth (1). The use of AI-driven technology has the potential to make a difference in each step of the IVF process, with the final goal of helping more people build the families they want.

Some ways in which AI can assist clinicians during the IVF process include:

  • Early identification of patients who might experience infertility.

  • More accurate ways to predict a patient's chance of success with IVF and the results to expect along the way.

  • More personalized treatment plans during ovarian stimulation.

  • Support for laboratory processes in embryo selection (choosing the most viable embryos).

  • Assessing the endometrium to optimize the chance of a successful pregnancy (1).

Here’s a Detailed Example for How AI May Help With IVF.

Ovarian stimulation is one of the first steps in IVF. Before ovarian stimulation, doctors conduct baseline tests – consisting of bloodwork and ultrasounds – to get an understanding of your ovarian reserve (the remaining supply of eggs in your ovaries). With that information, clinicians choose a protocol of IVF medications to stimulate your ovaries to develop multiple follicles during your menstrual cycle. While it initially might seem that more medication is better, that’s not always the case. Each patient has an optimal treatment plan to maximize their number of eggs and their chances of success, while minimizing the risks of over-stimulation.

This is where AI can assist fertility clinics, and where our team at Alife found an opportunity to provide information to clinicians that aims to help improve patient outcomes. Stim AssistTM is a set of clinical decision support tools to help clinicians maximize the number of MIIs (mature eggs) retrieved from your ovarian stimulation cycle.

First, Stim AssistTM's starting dose tool can assist clinicians in selecting your starting dose of ovarian stimulation medication. Our expert data scientists and engineers have developed AI algorithms that draw from tens of thousands of past patient IVF cycles to find the patients whose baseline characteristics (age, BMI, AMH, AFC) are closest to yours. With this information, the tool generates MII predictions for a range of starting doses of FSH medication. Since medications for IVF can cost thousands of dollars and most patients will need to undergo more than one IVF cycle, AI assistance in clinicans’ decision-making process may also help reduce the overall cost of IVF.

IVF Medication Dose Curve

Second, Stim AssistTM's trigger tool generates daily forecasts for MIIs and estradiol levels if the patient is given their trigger shot (which causes the eggs to mature) today versus tomorrow. This decision support tool provides clinicians with data that could assist them in maximizing the number of mature eggs retrieved each cycle.

While some studies have suggested that egg quality might decline as patients are stimulated to retrieve larger numbers of eggs, new research shows that that may not always be the case. In fact, retrieving more MIIs frequently correlates with better outcomes. By improving the number of eggs retrieved per cycle, patients’ chances of a live birth may increase.

IVF and other forms of reproductive medicine already give many people the chance to add to their family. AI has the potential to make that possible for more people. By aggregating and interpreting data, AI may help to improve fertility treatment outcomes, reduce costs, and make the entire IVF process more transparent.

Alife – a fertility technology startup based in San Francisco – uses artificial intelligence to improve IVF outcomes and expand global access to fertility care. If you’re a fertility clinician or clinic operator and would like a demo of our Alife AssistTM products, we’re always happy to hear from you!

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  1. Swain, J., VerMilyea, M. T., Meseguer, M., & Ezcurra, D. (2020). AI in the treatment of fertility: key considerations. Journal of Assisted Reproduction and Genetics, 37(11), 2817–2824. https://doi.org/10.1007/s10815-020-01950-z

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