Automation and optimization of multi-pulse laser zona drilling of mouse embryos during embryo biopsy
Bibliographic record
Abstract
Laser zona drilling (LZD) is a required step in many embryonic surgical procedures, for example, assisted hatching and preimplantation genetic diagnosis. LZD involves the ablation of the zona pellucida (ZP) using a laser while minimizing potentially harmful thermal effects on critical internal cell structures. OBJECTIVE: Develop a method for the automation and optimization of multipulse LZD, applied to cleavage-stage embryos. METHODS: A two-stage optimization is used. The first stage uses computer vision algorithms to identify embryonic structures and determines the optimal ablation zone farthest away from critical structures such as blastomeres. The second stage combines a genetic algorithm with a previously reported thermal analysis of LZD to optimize the combination of laser pulse locations and pulse durations. The goal is to minimize the peak temperature experienced by the blastomeres while creating the desired opening in the ZP. RESULTS: A proof of concept of the proposed LZD automation and optimization method is demonstrated through experiments on mouse embryos with positive results, as adequately sized openings are created. CONCLUSION: Automation of LZD is feasible and is a viable step toward the automation of embryo biopsy procedures. SIGNIFICANCE: LZD is a common but delicate procedure performed by human operators using subjective methods to gauge proper LZD procedure. Automation of LZD removes human error to increase the success rate of LZD. Although the proposed methods are developed for cleavage-stage embryos, the same methods may be applied to most types LZD procedures, embryos at different developmental stages, or nonembryonic cells.
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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".