Quantification of the visco-elastic properties of human lung tissue in the development of a tissue mimicking phantom for applications in thoracic and robot-assisted surgical simulation
Bibliographic record
Abstract
Surgical simulation permits the adoption of new skills and enables the development of new procedures and instrumentation. Essential to surgical simulation are materials with realistic imaging and force feedback properties that accurately simulate tissue behaviour. The visco-elastic properties of human lung tissue were quantified for the development of a tissue mimicking lung phantom for thoracic and robot-assisted surgical simulation. We set out to determine if the mechanical properties of human lung tissue can be determined using a simple instrument. A small, free-standing desktop instrument capable of employing and quantifying a normal indentation to a target tissue was developed and validated against the gold standard of compression testing. Six separate lung and tumour specimens were subjected to repeated cycles of indentation testing immediately after surgical resection. Each specimen was indented with an incremental indentation stress ranging from 0.01 to 0.1 Newtons (N). A force (N) versus displacement (mm) curve was generated as a reflection of the indentation data. The pooled data generated statistically distinct curves for the tumour and lung specimens. The resulting curve for the indentation of the resected lung specimens is defined by the equation y = 0.0863 × − 0.0267 (R2 = 0.996), and the indentation curve for the tumour specimens is defined by y = 0.1554 × − 0.0024 (R2 = 1). The mechanical properties of human lung tissue can be determined using a simple tissue indenter. To our knowledge, this is the first description and quantification of the visco-elastic properties of lung tissue. These data have been used to generate a prototype of an anatomically and mechanically accurate tissue-mimicking phantom.
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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".