Inanna Fertility applies machine learning and deep learning algorithms to IVF cycles to in order to optimize and tailor the process improving live birth rates.

Research currently shows that the probability of live birth per IVF cycles is ~17%. Unlike male fertility issues which have greatly benefited from ICSI-IVF technique, the adverse impact of women’s mature age on IVF success constitutes that things won’t go as planned even with the help of assisted reproductive technology “ART”. This becomes evident even in countries with high standards of care including Switzerland. Generally, fertility is declining with age and ART is unable to improve age-related reduction of fertility.

Research indicates that even a basic machine-learning model could produce much more accurate IVF success predictions than those made by the clinics today. While clinics primarily rely on age-based statistics and a doctor’s intuition, the artificial intelligence “AI” model can systematically factor in a couple’s relevant health data. 
Inanna uses machine learning algorithms to provide couples with a personalized prediction of their possible IVF outcomes. Our software will enable physicians to accurately recommend to their patients whether to proceed with IVF treatment. 

Inanna focuses on the following components of the IVF cycle: estimate the probability of live birth per patient per cycle, optimize the ovarian stimulation protocol, assess embryo quality, and improve uterine receptivity. The analysis is executed two ways: using machine learning algorithms and deep learning neural networks- please see the attached presentation for more details. 

Inanna has partnered with the world’s largest reproductive group in terms of data (we will receive data from them over the last 25 years) as well as medical expertise. We also have partnership with University of Zurich hospital (reproductive endocrinology department).