Automated Segmentation of M-Mode Lung Ultrasound Images Obtained from a Single-Element Wearable Ultrasonic Sensor
Bibliographic record
Abstract
Lung ultrasound (LUS) is an increasingly popular imaging method for lung disease diagnosis. Adoption is limited by the availability of trained experts to interpret the artefact-based LUS images. With the recent advent of automated LUS interpretation methods using machine learning and wearable ultrasound technology, the benefits of LUS can potentially be leveraged in novel ways that overcome this training shortage. In this work, automatic segmentation methods are investigated for motion-mode LUS images of a lung phantom and eight healthy human subjects obtained using a single-element wearable ultrasonic sensor (WUS). The proposed DBSCAN cluster boundary estimation method resulted in 95.6% and 92.0% median Jaccard index for the phantom and in-vivo images, respectively. The WUS, in combination with the proposed segmentation methods, has potential to enable operator-independent, continuous, and long-term lung monitoring.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".