All content on AvaWorld is fact checked to ensure impeccable accuracy
Here at Ava, we take data—especially fertility and pregnancy data—very seriously. Our team is led by premier data scientists, obstetricians, gynecologists, and reproductive endocrinologists, many of whom hold Ph.D.’s and decades of experience in their respective fields. So, when you read content on AvaWorld, you can trust that it’s backed by science and has been thoroughly researched and vetted by our experts.
Ava for healthcare professionals
Unprecedented insight from the single most comprehensive fertility predictor ever devised
Ava for healthcare professionals
Unprecedented insight from the single most comprehensive fertility predictor ever devised
Why do your patients need Ava?
Almost 75% of women don’t know the best time of their cycle to conceive
Wilcox demonstrated that intercourse timed during a woman’s fertile window could increase her probability of conception from 0.15 to 0.362
Ava’s algorithm is grounded in the largest continuously-updated data set of its kind.
Ava detects over 70 statistically significant differences in biometric parameters between menstrual cycle phases.
Previous
Next
5.3 fertile days vs. only 2.0 days with OPKs
Other methods detect fewer real-time fertile days. Ava displays 2.5 times more fertile days than provided by OPKs*.
* LH based
Ava uses machine learning and artificial intelligence to detect 5.3 fertile days
Commonly used cycle tracking apps have been shown to be inaccurate. Ava is effortless to use and detects when the fertile window both opens and closes in real time with almost 90% accuracy*.
*Accuracy is the percentage of days correctly classified as infertile or fertile.
See our results
Ava is a research-driven digital health company. Active in all areas of femtech, we develop science-based services for Ava’s users to become a companion for women throughout their whole reproductive age. Ava cooperates with international researchers in gynecology, obstetrics, reproductive endocrinology, computational science, machine learning, physiology, chronobiology, sensor technologies, data security and signal processing.