Advances in human oocyte in vitro maturation: current status and future perspectives: a narrative review
Bibliographic record
Abstract
Abstract: Oocyte in vitro maturation (IVM) is an evolving component of assisted reproductive technology (ART) that offers a less invasive and cost-effective alternative to conventional controlled ovarian stimulation. It is particularly beneficial for patients at risk of ovarian hyperstimulation syndrome (OHSS), those with polycystic ovary syndrome (PCOS), and individuals requiring urgent fertility preservation. Despite these advantages, clinical uptake has remained limited owing to concerns about the developmental competence and quality of IVM-derived oocytes. To address this, we conducted a comprehensive literature search of PubMed, Embase, and Web of Science for articles published between January 2000 and June 2025, using combinations of keywords related to IVM, oocyte maturation, culture protocols, oocyte quality, and clinical outcomes. Recent progress in the field has led to the development of biphasic culture systems, pre-IVM priming strategies, and the incorporation of regulatory factors such as C-type natriuretic peptide (CNP) and oocyte-secreted factors, all of which have contributed to improved oocyte maturation and embryo development. Nonetheless, variability in outcomes persists due to differences in patient selection, stimulation protocols, and laboratory practice. Continued optimisation of IVM culture systems and a deeper understanding of oocyte maturation mechanisms will be essential for enhancing clinical efficacy. Future research should prioritise standardisation, patient-tailored protocols, and systematic long-term outcome data to support wider adoption of IVM. This review provides a comprehensive overview of recent advances and ongoing challenges in human oocyte IVM, offering perspectives on future directions for clinical translation and improved ART outcomes. Lay summary: IVM is a fertility treatment that allows immature eggs to mature in the laboratory rather than within the body. This approach can be safer, simpler, and more affordable than traditional IVF, especially for women with polycystic ovary syndrome (PCOS), those at risk of ovarian hyperstimulation, or those who need to preserve their fertility quickly, such as cancer patients. Although IVM holds great promise, it is not yet widely used because its success rates are not as high as those of conventional methods. This review looks at the latest scientific progress to improve IVM, including better laboratory techniques, the use of natural hormones and growth factors, and new ways to support egg development outside the body. These advancements have helped improve the quality of eggs and embryos, but challenges remain. Differences in patient types, medications used, and lab practices can affect how well IVM works. More research is needed to make IVM more consistent and effective so it can become a routine fertility option for a broader population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".