Integration of Force Reflection with Tactile Sensing for Minimally Invasive Robotics-Assisted Tumor Localization
Why this work is in the frame
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Bibliographic record
Abstract
Tactile sensing and force reflection have been the subject of considerable research for tumor localization in soft-tissue palpation. The work presented in this paper investigates the relevance of force feedback (presented visually as well as directly) during tactile sensing (presented visually only) for tumor localization using an experimental setup close to one that could be applied for real robotics-assisted minimally invasive surgery. The setup is a teleoperated (master-slave) system facilitated with a state-of-the-art minimally invasive probe with a rigidly mounted tactile sensor at the tip and an externally mounted force sensor at the base of the probe. The objective is to capture the tactile information and measure the interaction forces between the probe and tissue during palpation and to explore how they can be integrated to improve the performance of tumor localization. To quantitatively explore the effect of force feedback on tactile sensing tumor localization, several experiments were conducted by human subjects to locate artificial tumors embedded in the ex vivo bovine livers. The results show that using tactile sensing in a force-controlled environment can realize, on average, 57 percent decrease in the maximum force and 55 percent decrease in the average force applied to tissue while increasing the tumor detection accuracy by up to 50 percent compared to the case of using tactile feedback alone. The results also show that while visual presentation of force feedback gives straightforward quantitative measures, improved performance of tactile sensing tumor localization is achieved at the expense of longer times for the user. Also, the quickness and intuitive data mapping of direct force feedback makes it more appealing to experienced users.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it