Between Science and the Dream of Becoming a Parent
The Role of Artificial Intelligence in In Vitro Fertilization (IVF)
Senior Embryologist, ESHRE Member: Iulia Raica
In vitro fertilization (IVF) remains one of the most effective treatments for infertility. However, success rates are still limited by biological variability and multiple clinical factors.
Embryo selection represents the critical stage of IVF, as only a small proportion of embryos have real implantation potential and the ability to develop into a live birth. The goal of embryo selection is to identify the most viable embryos from a larger cohort of fertilized oocytes, many of which are non-viable due to abnormal development or chromosomal imbalances.
Embryo implantation depends on several factors:
- Embryo quality
- Endometrial receptivity
- Hormonal balance
- Lifestyle factors
Among these, embryo quality remains the primary determinant of IVF success.
The Biological Limits of IVF Success
One of the most significant limiting factors in IVF is embryonic aneuploidy, strongly associated with maternal age.
In addition to chromosomal abnormalities, cytoplasmic defects in oocytes — including organelle dysfunction, altered gene expression, and lack of essential macromolecules — can significantly compromise embryo development, even when chromosomal status appears normal.
Structural chromosomal abnormalities such as Robertsonian translocations or inversions are another cause of infertility. These often present with a normal phenotype and are detected only through karyotyping. Y chromosome microdeletions in sperm can also reduce euploidy rates.
Understanding chromosomal defects is therefore essential in comprehensive infertility assessment. It helps patients understand their diagnosis and enables physicians to design targeted treatment strategies.
Artificial Intelligence in Reproductive Medicine
In this context, the integration of artificial intelligence (AI) into reproductive medicine offers powerful analytical and decision-support tools capable of optimizing clinical outcomes and standardizing laboratory processes.
Deep learning algorithms can automatically analyze embryo images and identify patterns associated with embryo viability — patterns that are often inaccessible to human visual assessment.
AI applications in IVF now include:
- Oocyte evaluation
- Sperm assessment
- ICSI optimization
- Time-lapse embryo monitoring
- Detection of abnormal fertilization
- Blastocyst ranking
- Non-invasive ploidy prediction
Some AI platforms demonstrate performance comparable to non-invasive genetic testing, with the future potential of estimating embryo ploidy without biopsy.
Why Embryo Selection Is Critical in IVF
Only a small percentage of embryos generated in IVF cycles have real implantation potential. Most embryos are excluded due to abnormal development or chromosomal aneuploidy.
Traditional embryo evaluation relies heavily on visual observation and embryologist experience. While valuable, this method has limitations:
- Subjectivity
- Inter-observer variability
- Difficulty detecting subtle abnormalities
- Limited standardization
Technological advancements now allow more objective and reproducible assessment methods.
AI in Oocyte Evaluation
Oocyte quality is fundamental for IVF success.
Using convolutional neural networks (CNNs), AI can assess:
- Oocyte texture and structure
- Zona pellucida quality
- Organelle organization
These systems provide more consistent and precise evaluations compared to manual assessment.
AI in ICSI (Intracytoplasmic Sperm Injection)
ICSI is a delicate procedure requiring high precision.
AI systems can:
- Identify optimal oocyte orientation
- Determine ideal injection points
- Achieve precision rates up to 99%
This reduces procedural errors and increases efficiency.
Detection of Abnormal Fertilization
After fertilization, normal embryos display two pronuclei. Abnormal embryos (0, 1, or 3 pronuclei) can be identified by AI systems with over 93% accuracy, allowing early exclusion of non-viable embryos.
Non-Invasive Ploidy Prediction
Preimplantation genetic testing (PGT-A) is effective but invasive and costly.
AI systems analyze time-lapse embryo images and detect patterns associated with aneuploidy, potentially reducing the need for embryo biopsy.
Platforms such as STORK and BELA demonstrate promising results.
Blastocyst Evaluation and Live Birth Prediction
At day 5–6, embryos reach the blastocyst stage. AI platforms like STORK (≈96% accuracy) and AIRE/ERICA (70–77% accuracy) analyze embryo images and clinical data to predict implantation success and live birth probability.
Broader AI Applications in IVF
AI is also used for:
- Sperm selection and motility analysis
- Ovarian stimulation personalization
- Laboratory workflow optimization
- Culture environment monitoring
These applications reduce time, cost, and variability.
Adoption Trends
Global surveys show a clear increase in AI use in IVF:
- 24.8% adoption in 2022
- Over 53% regular or occasional use in 2025
Over 80% of specialists plan to invest in AI within the next five years.
Ethical and Practical Challenges
Despite its benefits, AI implementation raises concerns:
- High costs
- Limited training
- Regulatory gaps
- Ethical considerations in embryo selection
AI must remain a decision-support tool — not a replacement for medical expertise.
Conclusion
Artificial intelligence has transformative potential in IVF by increasing precision, objectivity, and efficiency.While not a miracle solution, AI represents a major step toward personalized, accessible, and optimized reproductive medicine. Its success depends on balancing technological innovation with ethical responsibility and human expertise.
