Infertility is estimated to affect as many as 186 million people worldwide. One in every four couples in developing countries had been found to be affected by infertility.
Enter Artificial Intelligence.
The vast, complex and diverse datasets spread across patient medical and lifestyle histories can be combed, integrated and analyzed. When it comes to infertility treatments, a true-blue AI solution will leverage all such datasets, including clinical pregnancy outcome data, drug treatment regiments and other datapoints, to help doctors look at multiple treatment options for their patients. AI-based solutions can boost treatment success rates, enabling doctors to review patient data, based on the patient’s responses to treatments.
Mira Fertility Tracker
San Francisco-based Mira leverages artificial intelligence and machine learning to create a monitoring system, named Mira Fertility. Mira Fertility tracks ovulation and hormone levels, and suggests the probability for pregnancy at any given time. The user of Mira Fertility device has to urinate on disposable test swabs and pop them into the device. The data from the sample is analyzed, in conjunction with several other datasets, including a user’s lifestyle, diet and fitness. A linked app that delivers the likelihood for getting pregnant.
Adelaide-based LifeWhisperer uses a non-invasive AI-enabled image analysis solution to improve the selection of viable embryos for IVF implantation, and thereby, empower couples with improved IVF success rates. By leveraging deep learning and other tools, LifeWhisperer is able to identify morphological features that constitute a healthy embryo.
LifeWhisperer provides IVF clinics with on-demand access and upload of standard microscope images, where its proprietary selection models are applied, returning an instant report on the viability of each embryo.
Prague-based PragueIVF leverages AI in developing CATI (Cognitive automation of time-lapse images) that boosts pregnancy success rates by upto 30-40%. CATI sorts embryos according to selected morphokinetic criteria that are obtained from time-lapse systems. This plays an important role in IVF with aneuploidy screening (PGS) in preventing the misdiagnosis of mosaic embryos.
By combining CATI with PGS helps in enhancing success rates by 30-40%, meaning that a woman with a 20% chance of pregnancy will reach the rates of a younger woman who has 50-60% chance of success.
Univfy is a predictive analytics platform that combines machine learning and artificial intelligence, to help women more accurately know their chances of IVF success.
Univfy analyzes many factors in a patient’s fertility profile, such as age, body mass index, ovarian reserve test results, semen analysis, and clinical diagnoses. This is in order to give personalized probability of in vitro fertilisation (IVF) success. A secondary aim is to bring more transparency to the success and cost of IVF.
Mountain View-based YonoLabs has developed the that leverages machine learning algorithms to plot a monthly fertility chart for future fertility prediction. Using the YONO earbud, user’s body temperature data is collected overnight. Then, the YONO app analyzes the data collected from the earbud, using machine learning, to track and chart the fertility.