Automated robotic vitrification of embryos
Bibliographic record
Abstract
This paper reports the first robotic system for vitrification of mammalian embryos. Vitrification is a technique for preserving oocytes and embryos in clinical IVF (in vitro fertilization). The procedure involves multiple steps of stringently timed pick-and-place operation for processing an oocyte/embryo in vitrification media. In IVF clinics, vitrification is conducted manually by highly skilled embryologists. Processing one oocyte/embryo occupies the embryologist 15–20 minutes, depending on protocols chosen to implement. Due to poor reproducibility and inconsistency across operators, success rates and survival rates also vary significantly. Through collaboration with IVF clinics, we are in process to realize robotic vitrification and aim ultimately to standardize clinical vitrification from manual operation to fully automated robotic operation. Our robotic system is embedded with two contact detection methods to determine the relative Z positions of the vitrification micropipette, embryo, and vitrification straw. A 3D tracking algorithm is developed for visually servoed embryo transfer and real-time monitoring of embryo volume changes during vitrification. Excess medium is automatically removed from around the vitrified embryo on the vitrification straw to achieve a high cooling rate. Tests on mouse embryos demonstrate that the system is capable of performing vitrification with a throughput at least three times that of manual operation and achieved a high survival rate (88.9%) and development rate (93.8%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".