Gain more precise egg quality insights to inform your IVF treatment decisions.

MAGENTA™ reports provide personalized egg quality scores that correlate to each egg’s chance of forming a high-quality blastocyst (day 5 or 6 embryo), providing you with with a deeper understanding of how your unique egg quality influences your cycle outcomes.

In particular, this information can help guide plans for future cycles in the event that your current cycle is unsuccessful.

Until now, there has been no way to objectively assess egg quality.

The current standard of care for egg quality relies on statistics. We are working to change that.

The problem with using statistics is that it assumes all patients in the same age group have the same egg quality. This just isn’t realistic, as everyone has their own unique health factors. Our research has shown variability in egg quality even within the same cycle of one patient.

Each MAGENTA™ report contains:

✓ A record of the number of mature eggs retrieved and evaluated

✓ Each egg’s individual MAGENTA™ score 

✓ Images of each of your mature eggs

✓ Your MAGENTA™ score distribution for your cycle

MAGENTA™ reports empower you and your clinician with information about your own egg quality, helping to make treatment decisions that give you the best chances for a successful future pregnancy.


MAGENTA™ calculates personalized egg quality scores by analyzing images of your eggs through our AI-powered software

Our software has analyzed over 100,000 egg images and their reproductive outcomes to learn patterns that help predict if your egg will become a high-quality blastocyst.

AI is able to detect details within the images that are invisible to the human eye, which is why our technology can predict blastocyst outcomes more accurately than embryologists.1

For more information about MAGENTA™ and FERTILITY TREATMENTS:


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    * based on your personal egg quality assessment & statistical modelling

    REFERENCE: 1. Nayot D, Meriano J, Casper R, Krivoi A. 2020. An oocyte assessment tool using machine learning; Predicting blastocyst development based on a single image of an oocyte. 36th Annual Meeting of ESHRE – Copenhagen.