SonoAssist: Open source acquisition software for ultrasound imaging studies
Bibliographic record
Abstract
We present “SonoAssist”, an open-source acquisition software designed to facilitate the development of point-of-care ultrasound (POCUS) assistance systems by simplifying the data collection process. This software caters to research utilizing ultrasound images along with gaze data or probe movement measurements to tackle tasks like standard scan plane detection, anatomical landmark detection, and ultrasound probe guidance. Through SonoAssist’s simple interface, users can easily collect data from the following sensors: an ultrasound probe, an RGBD camera, an eye tracker, a screen recorder, and IMUs (Inertial Measurement Unit). Furthermore, SonoAssist timestamps data as they are acquired with a single time reference, removing the need for additional synchronization steps. To document the software’s performance, we characterized the synchronization between the ultrasound image and IMU data streams, the eye tracker accuracy, and the acquisition frequencies (ultrasound probe: 22 Hz, eye tracker: 87 Hz, external IMU: 100 Hz, screen recorder: 13Hz).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.026 |
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".