Metabolomic profiling by near-infrared spectroscopy as a tool to assess embryo viability: a novel, non-invasive method for embryo selection
Bibliographic record
Abstract
BACKGROUND: The morphology of an embryo has a limited predictive value for assessing viability and ongoing pregnancy, therefore new selection tools are needed to maintain success rates with single-embryo transfer (SET). In this study, we investigated if metabolomic profiling of biomarkers of embryo culture medium by near-infrared (NIR) spectroscopy has a correlation with ongoing pregnancy in SET. METHODS: A total of 333 patients scheduled for in vitro fertilization (IVF) with SET were included in the study. Embryos were selected for transfer by morphological criteria on Days 2 and 3 of in vitro culture, and left over culture media samples were analyzed by NIR spectroscopy. RESULTS: The NIR spectral analysis produced unique metabolomic profiles that correlated to an embryo's reproductive potential. Resulting relative viability scores between positive and negative pregnancy outcomes were statistically significant (P < 0.03). A logistic regression of factors correlated to pregnancy outcomes showed that maternal age, percent fragmentation and relative viability scores all demonstrated a relationship. The extent of the correlation was determined by accuracy computation, where the accuracy of assessing viable embryos on Day 3 by metabolomic profiling was 53.6% and the accuracy of the morphological selection was 38.5%. In addition, the positive predictive value of metabolomic profiling was 0.365 and the negative predictive value was 0.830. CONCLUSIONS: NIR metabolomic profiling of spent embryo culture media was able to distinguish viable embryos from non-viable embryos for reproduction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".