Embryo Culture Systems and Morphokinetics in IVF: The Role of Time-Lapse Monitoring and Artificial Intelligence in Embryo Selection

Ashouri Movassagh, Sanaz; Heidari, Mahnaz; Ashouri Movassagh, Sepideh; Sadeghi, Mohammad Reza

Embryo Culture Systems and Morphokinetics in IVF: The Role of Time-Lapse Monitoring and Artificial Intelligence in Embryo Selection

Authors

Sanaz Ashouri Movassagh ¹

Mahnaz Heidari ¹

Sepideh Ashouri Movassagh ²

Mohammad Reza Sadeghi ¹

¹ Department of Andrology and Embryology, Reproductive Biotechnology Research Center, Avicenna Research Institute, Academic Center for Education, Culture and Research (ACECR), Tehran, Iran

² Human and Animal Cell Bank, Iranian Biological Resource Center (IBRC), Academic Center for Education, Culture and Research (ACECR), Tehran, Iran

Abstract

Background: Embryo selection and culture conditions are critical determinants of success in assisted reproductive technology (ART). Despite advances, implantation and live birth rates following in vitro fertilization (IVF) remain suboptimal. This systematic review evaluated the effects of embryo culture media (sequential versus single-step), time-lapse monitoring (TLM), and artificial intelligence (AI)-based embryo selection on IVF outcomes. Methods: This systematic review followed PRISMA guidelines. PubMed, Scopus, and Web of Science were searched through June 2025. Twenty-nine studies involving over 18,500 IVF/intracytoplasmic sperm injection (ICSI) cycles were included. Methodological quality was assessed using RoB2, the Newcastle–Ottawa Scale, and CLAIM according to study design. Outcomes related to culture media, TLM-derived morphokinetics, and AI-based embryo assessment were synthesized narratively. Results: Sequential and single-step culture media produced comparable clinical outcomes, although single-step media showed a modest increase in blastocyst formation (57.1% vs. 53.2%; p=0.03). No significant differences were observed in clinical pregnancy or live birth rates. TLM was associated with higher ongoing pregnancy rates in descriptive analyses (45.1% vs. 34.8%; p<0.01) and reduced early pregnancy loss. AI-based embryo selection demonstrated strong predictive performance (AUC 0.76–0.91), although clinical validation varied across algorithms, indicating differences in predictive accuracy rather than consistent improvements in clinical outcomes. Conclusion: Culture media selection appears to have limited influence on clinical outcomes, whereas TLM and AI-assisted embryo selection show promise for improving embryo assessment. Integration of validated AI-driven TLM systems into IVF practice may enhance embryo selection efficiency. Further large, prospective studies are needed to establish standardized clinical protocols.

Keywords

Artificial intelligence

Culture media

Embryo evaluation

In vitro fertilization

Pregnancy rate

time

lapse imaging

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