Todo el contenido en AvaWorld está verificado para garantizar una precisión impecable
Aquí en Ava, tomamos los datos, especialmente los datos sobre fertilidad y embarazo, muy seriamente. Nuestro equipo está liderado por científicos de primera línea, obstetras, ginecólogos y endocrinólogos reproductivos, muchos de los cuales tienen doctorados y décadas de experiencia en sus respectivos campos. Por lo tanto, cuando lea contenido en AvaWorld, puede confiar en que está respaldado por la ciencia y que nuestros expertos lo han investigado y examinado exhaustivamente.
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.
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.