Microscopy image of human blastocyst. Image credit: Liu et al. (CC BY 4.0)
More than 50 million couples worldwide experience infertility. The most common treatment is in vitro fertilization (IVF). Fertility specialists collect eggs and sperm from the prospective parents. They combine the egg and sperm in a laboratory and allow the fertilized eggs to develop for five days into a multi-celled blastocyst. Then, the specialists select the healthiest blastocysts and return them to the patient's uterus.
Since 1978, more than 8 million children have been conceived through IVF. Yet, only about 30% of IVF attempts result in a successful birth. As a result, fertility patients often undergo multiple rounds of IVF, which can be expensive and emotionally draining. Several factors determine IVF success, one of which is the health of the blastocysts selected for transfer to the uterus. Specialists select the blastocysts using several criteria. But these human assessments are subjective and inconsistent in predicting which ones are most likely to result in a successful birth. Recent studies suggest artificial intelligence technology may help select blastocysts.
Liu et al. show that using artificial intelligence to assess blastocysts and fertility patient characteristics leads to more accurate predictions about which blastocysts are likely to result in a successful birth. In the experiments, the researchers trained an artificial intelligence computer program using pictures of 17,580 blastocysts with known birth outcomes and the parents' clinical characteristics. The model identified 16 parental factors associated with birth outcomes. The top 5 most predictive parental factors were maternal age, the day of blastocyst transfer to the uterus, how many eggs were present in the ovaries, the number of eggs retrieved and the thickness of the uterus lining. The program achieved the highest prediction of healthy births so far, compared to success rates listed in other studies.
Artificial intelligence-aided blastocyte selection using patient and blastocyst characteristics may improve IVF success rates and reduce the number of treatment cycles patient couples undergo. Before specialists can use artificial intelligence in their clinics, they must conduct confirmatory clinical studies that enroll patient couples to compare conventional methods and artificial intelligence.
