Automated Sperm Immobilization for Intracytoplasmic Sperm Injection
Bibliographic record
Abstract
Sperm immobilization is a requisite step in intracytoplasmic sperm injection (ICSI). Conventionally, sperm immobilization is performed manually, which entails long training hours and stringent skills. Manual sperm immobilization also has the limitation of low success rates and poor reproducibility due to human fatigue and skill variations across operators. This paper presents a system for fully automated sperm immobilization to eliminate limitations in manual operation. Integrating computer vision and motion control algorithms, the automated system is able to visually track a sperm and control a micropipette to immobilize the sperm. A robust sperm tail tracking algorithm is developed to locate the optimal position on the sperm tail for sperm immobilization. The system demonstrates: 1) an average sperm tail tracking error of 0.95 μm; 2) a sperm tail visual tracking success rate of 96%; 3) a sperm immobilization success rate of 88.2% (based on 1000 trials); and 4) a speed of 6-7 s per successful immobilization.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".