Multiactuator Haptic Feedback on the Wrist for Needle Steering Guidance in Brachytherapy
Bibliographic record
Abstract
Brachytherapy is a cancer treatment procedure where long needles are inserted toward an inner body target in order to deliver radioactive seeds that treat the cancer cells. Controlling the trajectory of the needle is very challenging as it deviates from a straight path during insertion. In this letter, we present the pilot study of usefulness of a wristband with haptic feedback designed to help surgeons guide the needle toward a desired destination. The wristband embeds eight miniature actuators distributed around the wrist. The actuators are controlled to generate different haptic stimuli, each of which informs the user about a necessary needle steering manoeuvre. We describe the design of the wristband and its evaluation in two distinct user studies. In the first study, we evaluate how accurately users can identify the vibration patterns. In the second study, we focus on how the user responds to these patterns while performing needle insertion into tissue in an environment with high cognitive visual load. The reported average success rate in identifying the haptic pattern and the success rate in performing the correct action during needle insertion are 86% and 72%, respectively. These results suggest that the device could work in tandem with a needle steering algorithm to help surgeons achieve high quality implants and develop needle steering skills.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".