An Admittance-Controlled Robotic Assistant for Semi-Autonomous Breast Ultrasound Scanning
Bibliographic record
Abstract
Ultrasound imaging has been shown to successfully diagnose and provide visual assistance during treatment of breast cancer. However, human operators (i.e., technicians and clinicians) provide limited repeatability when performing the imaging scans. Current state-of-the-art automated breast volume scanners (ABVS) have high repeatability but deform the breast tissue significantly, which is undesirable for percutaneous therapies such as brachytherapy. A semi-autonomous system is presented here which leverages the accuracy of a serial manipulator-design robotic assistant to maintain the ultrasound probe at an optimal angle and ensure stable contact with minimal tissue deformation. Positioning of the probe across the surface of the breast is left in the hands of the human operator and is enabled through an admittance controller for the robot. A feasibility study is performed through a comparison of imaging quality for ultrasound scans of a simulated seroma in a phantom tissue when performed with human-in-the-loop and fully-autonomous modalities. The system was evaluated in a user trial showing similar image quality performance to a fully-autonomous position-controlled scanning device.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".