Automation and Control of Embryo Trophectoderm Cell Biopsy at the Blastocyst Stage
Bibliographic record
Abstract
OBJECTIVE: This study aims to develop and validate a vision-based automation framework for performing trophectoderm (TE) cell biopsy on mouse embryos. METHOD: The proposed framework leverages widely available tools in research laboratories and In-Vitro fertilization (IVF) clinics, combined with computer vision and image-based control algorithms. A computer vision system first estimates the embryo's orientation to enable precise reorientation for zona pellucida (ZP) laser perforation. A vision-feedback control system then guides the embryo to the targeted perforation location and determines optimal laser parameters for ZP perforation. A vision-guided vacuum system aspirates the TE cells, with a multi-pulse laser ensuring their separation. RESULTS: Experimental validation using mouse blastocyst embryos demonstrated the feasibility and reliability of the proposed automation method. The vision-based approach achieved accurate orientation, controlled ZP perforation, and successful isolation of TE cells, effectively replicating manual biopsy techniques performed by skilled embryologists. CONCLUSION: The study presents a robust framework for automating embryo TE biopsy, reducing variability, and enhancing procedural precision. Integrating computer vision and control algorithms allows for consistent and reproducible results. SIGNIFICANCE: By utilizing existing infrastructure, the proposed method offers a cost-effective and scalable solution for single-cell research and IVF clinics, advancing genetic testing and reproductive medicine.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".