Automated Morphological Grading of Human Blastocysts from Multi-focus Images
Bibliographic record
Abstract
The live birth rate of in vitro fertilization (IVF) treatment cycles has remained stagnant at ~30% over the last decade. Yet with global demand for IVF treatment on the rise, there is mounting pressure to break through this long-standing plateau. The IVF community now turns its attention towards automated and data-driven Artificial Intelligence (AI) approaches for the critical task of blastocyst evaluation to potentially revolutionize the field. Despite it being routine for embryologists to grade blastocysts by viewing their morphology at multiple focal planes under a microscope, existing automated grading methods in literature predominantly use blastocyst images from a single focal plane as input. In this thesis, we design a novel CNN-based model architecture to harness the comprehensive morphology presented in multi-focus images for morphological grade prediction. Our method predicted the three grades (developmental stage, inner cell mass morphology, trophectoderm morphology) assigned to each blastocyst with ROC AUCs of 0.98, 0.95, and 0.97. Notably, our model outperformed comparable single-image models, as well as the mean performance of embryologists.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".