Robotic Blastocyst Biopsy
Bibliographic record
Abstract
Soft tissue cutting is used for incision, separation and removal of tissues or cells. Due to the high deformation of soft tissues resulting from their viscoelasticity, 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 (TE) cells from a blastocyst (Day 5 embryo). The system, for the first time, enables TE cell junction detection for laser ablation throughout the blastocyst biopsy process by using U-Net with a modified Dice loss. A dynamics model taking into account cell mechanical properties was established to describe the motion of the TE cells inside a biopsy micropipette. Based on the model, an integral-based adaptive control method was developed for TE 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 update law. The biopsy success rate was 100% and the average biopsy time is 25 s. Compared to manual blastocyst biopsy, the robotic biopsy system reduced the necessary number of laser pulses ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 5 vs. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 7), reduced the number of lost nuclei in the collected TE cells (8% vs. 36%), and shortened the blastocyst.s recovery time (35 min vs. 50 min), indicating lower invasiveness to both the blastocyst and the collected TE 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".