Sonographic Assessment of Hyperechoic Vertical Artifact Characteristics in Lung Ultrasound Using Microconvex, Phased Array, and Linear Transducers
Bibliographic record
Abstract
Hyperechoic vertical artifacts are an essential feature of lung ultrasound (LUS) arising from various pathological states. Those that meet the criteria for B-lines have the most significant diagnostic value and should be differentiated from other hyperechoic vertical artifacts of unspecified clinical importance. Although numerous studies have assessed the impacts of transducer type on the appearance of B-lines in human medicine, comparative studies in veterinary medicine are limited and conflicting. This study compares three transducer types for the assessment of hyperechoic vertical artifacts in dogs. We hypothesize that there is high-level reviewer agreement in the assessment of HVA image quality and characteristics, and that the image quality/characteristics differ between the three transducers. Dogs (n = 8) with HVAs and sonographic absence of lung consolidations, pleural effusion, and/or pneumothorax were enrolled. Twenty-four cine-loops (5 s) containing HVAs were retrospectively and independently reviewed by two reviewers, who were blinded to the case details but not transducer type. The reviewers assessed the cine-loops for the following: whether HVAs meet the B-line criteria, ease of counting HVAs, and overall image quality. Paired cine-loops from the same patient using different transducers were then presented for HVA quality comparison. Inter-rater concordance was determined using the Kappa coefficient, Kendall’s tau, and Pearson correlation coefficient, while characteristics were compared using chi-square and Kruskal–Wallis tests (level of significance, α = 0.05). The overall concordance of image quality was good (Pearson’s coefficient = 0.82). The PA transducer scored lower in image quality (p < 0.001), HVA blending (p = 0.014), graininess (p < 0.001), and clarity of edges (p < 0.001) when compared with the microconvex and linear transducers, and the identification of B-line criteria differed between transducers (p = 0.024). Furthermore, the PA scored lowest in the comparison of paired cine-loops regarding the image and HVA quality (p < 0.001). Although more HVAs failed to reach the far field with the linear transducer (10/16, 62.5%) compared with the microconvex (8/16, 50%) and PA (3/16, 18.5%) transducers, the linear transducer scored higher than the microconvex and PA transducers regarding its ability to count B-lines (p < 0.001). This study demonstrates that the type of transducer significantly impacts the characteristics of HVAs, with the PA transducer producing lower-quality images compared with the microconvex and linear transducers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".