Development of an Instrumented Surgical Setup for Quantifying Displacement and Force in Surgical Dissection
Bibliographic record
Abstract
Knowledge of positional and force properties of surgical dissection in neurosurgery is essential in developing simulation platforms for neurosurgical training such that realistic motion and perception can be conveyed to the trainee during practice. Most proposed models in literature utilize computational techniques to formulate required parameters. However, these models are not realistic enough compared to data obtained from experiments on real brain. Therefore, developing a setup to measure the position, orientation, and interaction forces will help researchers formulate realistic parameters. This paper presents the development of such a setup for quantification of displacements and tool-tissue interaction forces during performance of microsurgical tasks. A bipolar forceps is equipped with a set of force sensing elements to measure the tool-tissue interaction force components. The position and orientation of the forceps tips are measured by attaching a tracker to the bipolar forceps. To show proof-of-concept, an experienced surgeon and one assistant surgeon performed 35 neurosurgical tasks (320 trials) on a cadaver brain (previously-frozen) using the instrumented setup. Positional and force data of the bipolar forceps were recorded during surgical dissection of different brain structures. This paper reports results collected from two microsurgical tasks over 40 trials: dissection of sylvian cistern arachnoid (SCA) and dissection of middle cerebral artery (MCA). Results showed that the mean values of interaction forces during dissection of MCA were smaller than dissecting SCA. The maximum forces observed were 1.94 N and 1.75 N for SCA and MCA, respectively. The application of quantifying such parameters using the developed setup will be in training neurosurgery residents using surgical simulators in which the knowledge of brain tissue parameters is required to formulate the tissue model.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".