Usability of Two Ultrasound Tongue Imaging Devices in Speech-Language Pathology
Bibliographic record
Abstract
PURPOSE: Ultrasound tongue imaging is a promising tool in speech-language pathology; however, little is known about the usability of ultrasound scanners for speech-language pathologists (SLPs), who typically have low familiarity with ultrasound imaging. This study looks at the usability of two ultrasound scanner models for SLPs: a Sonosite all-in-one scanner with a wired probe, and a Clarius wireless probe scanner, used with a tablet app. METHOD: Twelve SLPs and phonetics experts (all female) participated in two filmed sessions in our lab where they learned to use the two models of scanners with custom-written manuals. Each scanner was used in each session to complete a simple task including recording videos of their or the experimenter's tongue. After each use of a scanner, participants completed a modified and translated version of the System Usability Scale. The time required to complete the task was measured. Two expert judges rated the quality of the video recordings. RESULTS: Participants took less time to complete the task and improved their choice of settings from the first to the second session, regardless of the scanner being used. In the usability scale, SLPs showed a higher satisfaction with the wireless tablet interface than with the all-in-one ultrasound interface. The tablet interface with the wireless scanner also allowed better choices of settings. However, in the second session, positioning of the probe was better with the all-in-one scanner, which has a smaller and lighter probe. CONCLUSIONS: For SLPs, the usability of a wireless ultrasound scanner with a mobile application seems better than that of an all-in-one scanner. However, its cumbersome probe seems to hamper probe positioning. Future studies should include a lightweight ultrasound scanner that connects via USB to a laptop or tablet and provide image interpretation training for the SLPs.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".