Wearable ultrasound for continuous lung monitoring and pneumothorax detection
Bibliographic record
Abstract
Pneumothorax (PTX) is an acute respiratory condition in which air accumulates between the lung and chest wall. PTX can become life threatening if not identified and treated; therefore, early detection and prompt intervention are critical for patients at risk. Lung ultrasound (LUS) is a real-time, portable imaging modality suitable for bedside use. In contrast to chest X-ray for PTX detection, LUS does not use ionizing radiation and has demonstrated superior diagnostic performance. However, the operator dependence of handheld ultrasound probes limits the feasibility of continuous PTX monitoring which would enable early detection of PTX and be particularly beneficial for patients at ongoing risk, such as those under mechanical ventilation or aeromedical transport. We have developed a flexible, lightweight, and thin wearable ultrasonic sensor (WUS) for motion mode (M-mode) ultrasound image acquisitions. The WUS consists of a single-element ultrasonic transducer made of a polyvinylidene difluoride piezoelectric polymer film. Its simple fabrication and component materials make it low-cost and suitable for disposable use. The WUS is well-suited for long-term, continuous monitoring due to its hands-free operation. In this study, we demonstrate the feasibility of the WUS for PTX detection by using M-mode image features with lung tissue-mimicking phantoms and in-vivo human subjects. [Work supported by the Natural Sciences and Engineering Research Council of Canada.]
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".