Emerging technologies for improving embryo selection: a systematic review
Bibliographic record
Abstract
Yasemin Sengul,1 Ayse Bener,2 Asli Uyar3 1Computer Programming Program, Advanced Vocational Studies, Dogus University, Acibadem, Kadikoy, Istanbul, Turkey; 2Data Science Laboratory, Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON, Canada; 3Department of Computer Engineering, Okan University, Tuzla, Istanbul, Turkey Background: Embryo selection procedure is one of the critical success factors in in vitro fertilization treatment. Various embryo selection technologies have emerged within the past decade. These technologies are either used in combination with morphology or introduced to replace the conventional morphological evaluation. This review aims at investigating the effect of these novel embryo selection technologies on in vitro fertilization success rates. Methods: A systematic review of the literature was performed among full-text English articles in the PubMed database. Study selection was based on the predefined inclusion and exclusion criteria. Clinical effectiveness of the selected studies was measured in terms of implantation, pregnancy, live birth, and multiple pregnancy rates. Results: Five studies were identified that fitted the inclusion criteria. In these studies, researchers used aneuploidy screening, metabolomic profiling, and time-lapse imaging analysis as the new technologies. Among these studies, the one that conducted a randomized controlled trial of a commercial time-lapse imaging system demonstrated significant improvement in implantation rate. Conclusion: Studies using emerging technologies for embryo assessment provide promising results in retrospective analysis. On the other hand, randomized controlled trial studies that test the efficacy of novel embryo selection techniques in clinical practice failed to demonstrate a consistent improvement in the resulting success rates. This review provides a snapshot of the most recent literature on embryo assessment and embryo selection studies. Our findings show that there is a lack of comparative measurements and analyses that are able to assess benefits of the novel technologies in the field of embryo selection. Keywords: embryo selection, in vitro fertilization, IVF
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".