Enhancing needle puncture detection using high-pass filtering and diffuse reflectance
Bibliographic record
Abstract
Introduction Chest trauma or disease progression can lead to tension pneumothorax, a condition where mounting pressurization of the pleural cavity (the space between the chest wall and the lungs) leads rapidly to cardiac arrest. In pre-hospital settings, tension pneumothorax is treated by venting the pleural cavity via a needle introduced through the chest wall. Very high failure rates (up to 94.1%) have been reported for pre-hospital needle decompression, however, and the procedure can result in the accidental puncture of critical thoracic tissues because it is performed blind. Instrumented needles could help operators more reliably identify when the tool has entered the target space. Methods This paper investigates technical approaches to provide such support; we created an experimental system that acquires needle force and position signals, as well as the diffuse backscattered reflectance from white light carried to and collected from the needle’s tip via two in-bore optical fibers. Data collection occurred while two experimenters inserted a bevel-tipped percutaneous needle into an ex vivo porcine rib section simulating human chest anatomy. Four data-driven puncture-detection (DDPD) algorithms from the literature, which are appropriate for use with the variable tool velocities produced by manual insertions, were applied to the resulting data set offline. Grid search was performed across key signal-processing parameters, high-pass filters (HPFs) were applied to examine their impact on puncture detection, and a first exploration of multimodal (ensemble) methods was performed. Results Combining high-pass filters with DDPD methods resulted in a 2.7-fold improvement (from 8.2% to 21.9%) in the maximum overall precision (MOP) produced by force signals. Applying this HPF + DDPD scheme to reflectance data streams yielded a peak MOP of 36.4%, and combining reflectance with force generated the best MOP overall (42.1%); these results represent 4.4-fold and 5.1-fold improvements, respectively, over the best MOP produced by the traditional application of DDPD algorithms to force signals alone. Discussion These results strongly support the utility of high-pass filters combined with both reflectance-only and multimodal reflectance-plus-force data-driven puncture-detection schemes for needle decompression applications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".