Sci‐PM Fri ‐ 04: A three‐dimensional micro‐ultrasound image‐guided and robotically assisted needle positioning system
Bibliographic record
Abstract
Biomedical research using small animals often requires interventional procedures such as biopsies and injections. When performed inaccurately, these procedures can produce poor results, jeopardizing the study. A needle‐positioning robot for three‐dimensional micro‐ultrasound guided interventions was developed to improve the accuracy of needle insertion. The robot has three degrees of freedom for positioning the needle in three dimensions. Two rotational joints are used to control orientation (roll and pitch), while another joint linearly translates the needle to perform insertion. The three axes intersect at a single point, producing a remote centre of motion (RCM) that acts as a fulcrum for the three‐dimensional orientation of the needle. The RCM corresponds to the insertion point of the needle into the animal. The robot was calibrated to ensure that the three axes intersected at a single point, and that the needle tip was positioned at the RCM. The calibration was performed using a high‐resolution digital camera to find the centre of rotation about the pitch and roll axes separately. The position of the needle was adjusted until it was aligned with the RCM. Calibration results indicate that the distance from the needle to the pitch and roll axes are 24±30 μm and 18±15 μm, respectively. The pitch and roll axes are separated by 11±3 μm. The calibration procedure allowed us to detect and correct manufacturing and design errors. The expected maximum needle positioning error computed from the calibration is 32 μm when the needle tip is moved to the boundary of the workspace.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".