Robotic Cell Manipulation for Blastocyst Biopsy
Bibliographic record
Abstract
Soft tissue cutting is used for incision, separation and removal of tissues or cells. Due to high deformation of soft tissues resulting from their viscosity and elasticity, it is challenging to accurately cut the tissue along a desired path and control the force applied to the tissue for reducing invasiveness, especially at the microscale. This paper presents a robotic biopsy system for cutting and collecting trophectoderm cells from a highly deformable blastocyst. The system, for the first time, enables TE cell junction detection for laser ablation throughout the blastocyst biopsy process by using a convolutional neural network. The overall detection error was 2.13% in every 1,000 cell junctions with position RMSE of$1.63\ \mu \mathrm{m}\pm 0.29\ \mu \mathrm{m}$. A dynamics model was developed to describe the motion of the trophectoderm cells inside a biopsy micropipette. Based on this model, an adaptive control method was developed for trophectoderm cell aspiration and positioning inside the biopsy micropipette. Experimental results revealed that the controller was capable of effectively compensating for the cell positioning error by updating the varying system parameters according to the adaptation law. The success rate was 100%, the cell aggregate positioning accuracy was$\pm 1\ \mu \mathrm{m}$, the average settling time was 2 s, and the largest overshoot was$4.3\ \mu \mathrm{m}$. Compared to manual blastocyst biopsy, the robotic biopsy system shortened the blastocyst's recovery time (35 min vs. 50 min) which indicates lower invasiveness.
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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 | 0.000 | 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) | 0.002 | 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".