Label-free 3D subcellular phenotyping of mouse embryos by holotomography enables early prediction of blastocyst formation
Bibliographic record
Abstract
Abstract Accurate embryo quality assessment is central to improving outcomes in in vitro fertilization (IVF), yet current practice relies mainly on subjective two-dimensional (2D) morphology. Here we present a label-free framework for quantitative three-dimensional (3D) embryo phenotyping using low-coherence holotomography (HT). Time-lapse HT enabled volumetric imaging of mouse embryos from the 2-cell stage to the blastocyst without affecting developmental competence, capturing subcellular features at high resolution. Quantitative analysis revealed that matured embryos exhibited higher blastomere counts, greater spatial variability, and tighter nuclear packing, whereas arrested embryos showed enlarged blastomeres, elevated cytoplasmic heterogeneity, and fewer, larger nuclei. Machine learning models trained on these features achieved robust prediction of blastocyst formation (AUC up to 0.958). Together, these findings demonstrate that HT provides objective and interpretable 3D biomarkers that could augment and transform embryo selection in IVF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".