For years, freezing eggs meant also freezing an unknown. Women who opted for vitrification as a strategy to preserve their fertility knew how many oocytes they stored, but not what real potential they had. The estimate depended almost exclusively on age and population statistics. There wasn’t much else.
That’s starting to change. Artificial intelligence has begun to be used in reproductive medicine not only to optimize technical processes, but also to offer patients more precise predictive information about their real chances of becoming mothers in the future from their own vitrified eggs.
From visual intuition to algorithmic precision. Before the introduction of AI, the quality of an oocyte could hardly be objectively assessed. According to Dr. Marcos Meseguer, Global Director of Embryology Researchin an interview for Xatakathe assessment depended on general morphological criteria and the embryologist’s subjective impression, often based on whether the egg “looked pretty or ugly.”
There were no solid quantitative standards or models capable of estimating the biological competence of the oocyte. The prediction was extremely limited, almost equivalent to chance. The technological leap has not been incremental, but qualitative. “We have gone from having practically no prognostic tools to having models with real prediction capacity,” Meseguer details. Today, algorithms are introducing a layer of quantitative analysis that transforms that scenario.
More in depth. The change is not minor. As Meseguer explains to us, AI allows us to analyze thousands of images of oocytes whose subsequent clinical results are known—if they formed an embryo, if they reached a blastocyst—and learn patterns associated with reproductive success. The algorithm always evaluates the same parameters in a standardized way. This systematization eliminates variability between observers and converts a subjective assessment into an objective and reproducible evaluation. In other words, for the first time a probabilistic estimate can be offered based on data and not just general statistics by age.
It’s not magic: measure better, don’t see more. It is important to clarify what exactly AI does and what it does not do. The algorithm does not detect hidden genetic abnormalities nor does it replace tests such as preimplantation genetic diagnosis. As the specialist clarifies, the genetic analysis is not performed on the oocyte, but on the embryo after fertilization.
The AI applied to the oocyte analyzes the same images that the embryologist sees, but in a quantitative way. It accurately measures parameters such as oocyte diameter, the thickness of the zona pellucida or certain characteristics of the cytoplasm. “The difference is not seeing more, but measuring better and in a standardized way,” says Marcos Meseguer. Furthermore, the oocyte is not evaluated dynamically, as is the case with the embryo, but rather statically. It is not about choosing an “ideal candidate”—all mature oocytes are used in assisted reproduction—but rather about stratifying their biological potential and offering probabilistic estimates of competence.
More information, but no guarantees. This advance does not imply that laboratories “select” only the best oocytes. All mature oocytes (metaphase II) continue to be used. The difference is in the stratification of their biological potential. In fertility preservation—women who vitrify eggs for use years later—this information takes on special relevance. Instead of basing expectations solely on age, personalized data derived from algorithmic analysis can be incorporated.
However, caution is key. Age continues to be the most determining prognostic factor. AI does not modify biology or compensate for physiological limitations. It is a support tool, not a miracle solution, warns the expert. What it does achieve is reduce uncertainty. And in a field marked by emotional stress and complex decision-making, having quantified and objective information can change the clinical conversation.
A global trend towards automation. The incorporation of artificial intelligence is part of a broader transformation of fertility laboratories. A recent example picks it up The New York Times from a study published in Nature Medicine. The work analyzes a microfluidic device called OvaReadycapable of recovering eggs that the conventional method did not detect after follicular aspiration.
In the study, the device analyzed follicular fluid that had already been examined manually. In more than half of the patients, additional oocytes were found that were going to be discarded. The birth of a girl was even documented from one of those recovered oocytes. Although this technology is not exactly a predictive system like image analysis algorithms, it illustrates a clear trend: laboratories are incorporating automated tools that standardize processes and reduce exclusive dependence on human judgment.
Experts quoted by the American newspaper highlightHowever, larger studies are still needed to confirm that these additional eggs consistently increase the live birth rate.
The real impact: better managing the “biological clock”? Technological enthusiasm, however, has boundaries. “AI is a tool to support diagnosis and decision-making, not a miracle solution,” says the specialist in the interview. It can optimize decisions and reduce variability, but it cannot modify the intrinsic quality of gametes or alter biological limitations. In other words, it improves the information available, but does not change the biology.
The next step. Development does not stop at the oocyte evaluation. According to the embryologist, the next big leap will be the progressive optimization of ovarian stimulation protocols through predictive models that integrate clinical, hormonal and previous response data. More than “absolute customization”, it will be a continuous improvement in precision. Reproductive medicine is moving toward increasingly data-driven decisions.
In economic terms, technological incorporation may initially entail a higher cost, but in the medium and long term it could reduce failed cycles and make the system more cost-effective.
Freezing eggs without freezing uncertainty. Vitrification will continue to be a bet with a margin of uncertainty. No algorithm can promise a future pregnancy. But it can offer a more refined estimate of the biological potential of those frozen eggs.
For years, fertility preservation was a decision supported by general statistics. Today it is also beginning to rely on personalized predictive models. Artificial intelligence does not eliminate the passage of time or guarantee motherhood. But it does introduce something new to a discipline historically marked by probability: measurable, standardized, data-driven information.
And in a field where uncertainty weighs as much as biology, having more precise information can be, in itself, a profound change.
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