Lung ultrasound: A comparison of image interpretation accuracy between curvilinear and phased array transducers
Bibliographic record
Abstract
Introduction: Both curvilinear and phased array transducers are commonly used to perform lung ultrasound (LUS). This study seeks to compare LUS interpretation accuracy of images obtained using a curvilinear transducer with those obtained using a phased array transducer. Methods: We invited 166 internists and trainees to interpret 16 LUS images/cineloops of eight patients in an online survey: eight curvilinear and eight phased array, performed on the same lung location. Images depicted normal lung, pneumothorax, pleural irregularities, consolidation/hepatisation, pleural effusions and B-lines. Primary outcome for each participant is the difference in image interpretation accuracy scores between the two transducers. Results: A total of 112 (67%) participants completed the survey. The mean paired accuracy score difference between the curvilinear and phased array images was 3.0% (95% CI: 0.6 to 5.4%, P = 0.015). For novices, scores were higher on curvilinear images (mean difference: 5.4%, 95% CI: 0.9 to 9.9%, P = 0.020). For non-novices, there were no differences between the two transducers (mean difference: 1.4%, 95% CI: -1.1 to 3.9%, P = 0.263). For pleural-based findings, the mean of the paired differences between transducers was higher in the novice group (estimated mean difference-in-differences: 9.5%, 95% CI: 0.6 to 18.4%; P = 0.036). No difference in mean accuracies was noted between novices and non-novices for non-pleural-based pathologies (estimated mean difference-in-differences: 0.6%, 95% CI to 5.4-6.6%; P = 0.837). Conclusions: Lung ultrasound images obtained using the curvilinear transducer are associated with higher interpretation accuracy than the phased array transducer. This is especially true for novices interpreting pleural-based pathologies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".