Precision ZP Perforation Automation: A Vision-Based Robotic Approach for Blastocyst Embryo Biopsy
Bibliographic record
Abstract
Microsurgical operations such as embryo biopsy require extracting a material sample from inside the embryo for genetic testing. A precise perforation for embryo zona pellucida (ZP) is required to permit access for the biopsy micropipette to extract the sample. Many approaches have been developed for the ZP perforation, such as mechanical, chemical, and photothermolysis (the laser). This paper presents an automatic method for the blastocyst embryo ZP perforation. This method utilizes a conventional laser system currently in use in manual approaches in research labs and in-vitro fertilization clinics and controls the whole perforation process using a vision feedback system. An experimental setup is developed to verify the behavior of the proposed method, in which a holding micropipette is used to hold and move the embryo, which is then moved in two coordinate directions toward the laser spot location. A computer vision algorithm is used to estimate the embryo ZP thickness to tune the laser parameters and estimate the ZP circle median quadrant coordinates. Then use this information as a feedback signal to a simple proportional controller to control the embryo motion. Experimental results demonstrate that the system is capable of embryo relocation and ZP perforation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".