Background: Embryo selection in in vitro fertilization (IVF) aims to prioritize embryos with the highest reproductive potential. While preimplantation genetic testing for aneuploidy (PGT-A) remains the gold standard for identifying euploid embryos, it is invasive and not universally applicable. Deep learning (DL)-based models, such as the intelligent data analysis (iDA) score, have emerged as non-invasive alternatives for embryo assessment. This review critically evaluates the relationship between iDAScore (versions 1.0 and 2.0), embryo euploidy, and clinical outcomes, including live birth and miscarriage rates. Methods: A narrative review was performed using PubMed and Google Scholar, covering studies published from January 2020 to May 2025. The search included terms such as “iDAScore,” “deep learning,” “euploidy,” and “live birth.” Only English-language full-text studies assessing the predictive performance of iDAScore relative to chromosomal status or reproductive outcomes were included. Results: Six retrospective studies met the inclusion criteria. All reported a statistically significant association between higher iDAScore values and embryo euploidy. AUC values for euploidy prediction ranged from 0.60 to 0.68. In several studies, iDAScore was also positively associated with live birth rates and negatively with miscarriage rates. However, the predictive accuracy was moderate when restricted to euploid embryo cohorts, indicating that iDAScore may be more effective in broader populations where chromosomal status is unknown. Conclusions: iDAScore represents a promising adjunct to traditional embryo assessment. Although it cannot replace PGT-A, it may aid in embryo prioritization when genetic testing is not feasible. Larger prospective studies are warranted to further validate its clinical utility.

Embryo selection remains one of the main unresolved challenges in in vitro fertilization, despite major advances in assisted reproductive technologies. Conventional assessment is still largely based on morphological evaluation, which is limited by subjectivity, static observation, and the difficulty of integrating heterogeneous clinical and biological data. In recent years, artificial intelligence has emerged as a decision-support tool in embryology, enabling the analysis of large datasets derived from embryo images, morphokinetic parameters, and clinical variables. This review summarizes current approaches to artificial intelligence in embryo selection, including models based on static images and time-lapse imaging data. Machine learning and deep learning techniques are discussed, including convolutional neural networks and spatiotemporal models. The evaluation of model performance is also examined, highlighting the clinical relevance of endpoints such as time to live birth compared with traditional outcome measures. Finally, ethical and clinical implications are considered, including issues related to transparency, responsibility, human oversight, and regulation. Artificial intelligence has the potential to improve embryo selection, although further validation and standardized implementation are needed before routine clinical use.