Artificial intelligence transforms embryo health assessment in in vitro fertilization

Infertility affects nearly one in six couples worldwide, and in vitro fertilization (IVF) has become an essential tool in reproductive medicine. However, IVF success rates remain relatively low, averaging around 30%, largely due to the difficulty of selecting the healthiest embryos for transfer. Traditionally, embryo assessment has relied on embryologists’ visual inspection of morphology and growth patterns, a process that is often subjective, variable, and limited by human expertise.

Artificial intelligence (AI), particularly deep learning and computer vision, is now being applied to improve embryo health evaluation. By analyzing embryo images at various developmental stages—including the early cleavage stage, blastocyst stage, and full developmental process—AI systems reduce subjectivity and enhance consistency. Recent studies have shown that AI models can outperform manual assessments in accuracy, efficiency, and reproducibility, offering new opportunities to raise IVF success rates.

AI-based approaches range from semi-automated image segmentation to fully automated end-to-end models. At the blastocyst stage, where embryo features are more distinct, deep learning models have achieved accuracy rates as high as 92%. Time-lapse imaging, when combined with AI analysis, has proven especially valuable, capturing dynamic developmental changes and yielding more precise predictions of embryo viability. Multimodal approaches that integrate embryo imaging with patient clinical data further enhance personalized decision-making in reproductive medicine.

Despite these advances, challenges remain. Many models rely on institution-specific datasets, limiting generalizability. Issues such as small sample sizes, lack of interpretability, and inconsistent grading criteria across clinics also hinder clinical adoption. Ethical concerns—including patient autonomy, data privacy, and equitable access—must also be addressed. Future directions emphasize federated learning, multimodal data integration, and explainable AI techniques to ensure responsible and widespread implementation.