Input and output for surgical simulation: devices to measure tissue properties in vivo and a haptic interface for laparoscopy simulators.
Bibliographic record
Abstract
Current efforts in surgical simulation very often focus on creating realistic graphical feedback, but neglect some or all tactile and force (haptic) feedback that a surgeon would normally receive. Simulations that do include haptic feedback do not typically use real tissue compliance properties, favoring estimates and user feedback to determine realism. When tissue compliance data are used, there are virtually no in vivo property measurements to draw upon. Together with the Center for Innovative Minimally Invasive Therapy at the Massachusetts General Hospital, the Haptics Group is developing tools to introduce more comprehensive haptic feedback in laparoscopy simulators and to provide biological tissue material property data for our software simulation. The platform for providing haptic feedback is a PHANToM Haptic Interface, produced by SensAble Technologies, Inc. Our devices supplement the PHANToM to provide for grasping and optionally, for the roll axis of the tool. Together with feedback from the PHANToM, which provides the pitch, yaw and thrust axes of a typical laparoscopy tool, we can recreate all of the haptic sensations experienced during laparoscopy. The devices integrate real laparoscopy toolhandles and a compliant torso model to complete the set of visual and tactile sensations. Biological tissues are known to exhibit non-linear mechanical properties, and change their properties dramatically when removed from a living organism. To measure the properties in vivo, two devices are being developed. The first is a small displacement, 1-D indenter. It will measure the linear tissue compliance (stiffness and damping) over a wide range of frequencies. These data will be used as inputs to a finite element or other model. The second device will be able to deflect tissues in 3-D over a larger range, so that the non-linearities due to changes in the tissue geometry will be measured. This will allow us to validate the performance of the model on large tissue deformations. Both devices are designed to pass through standard 12 mm laparoscopy trocars, and will be suitable for use during open or minimally invasive procedures. We plan to acquire data from pigs used by surgeons for training purposes, but conceivably, the tools could be refined for use on humans undergoing surgery. Our work will provide the necessary data input for surgical simulations to accurately model the force interactions that a surgeon would have with tissue, and will provide the force output to create a truly realistic simulation of minimally invasive surgery.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".