Exploring Eye‐tracking Technology as an Assessment Tool for Point‐of‐care Ultrasound Training
Bibliographic record
Abstract
OBJECTIVES: Eye-tracking technology has emerged as a potentially useful learner assessment tool in several medical specialties. In the fields of general surgery and anesthesiology, it has been shown to reliably differentiate between different levels of expertise in procedural skills. In the field of radiology, it has been shown to be a valid assessment tool for diagnostic test interpretation. Current methods of competency assessment in point-of-care ultrasound (POCUS) remain a challenge, because they require significant direct observation time by an instructor. The purpose of this study was to determine if eye-tracking technology can accurately distinguish between novice and experts in the interpretation of POCUS clips, specifically of the focused assessment using sonography in trauma (FAST) scan. METHODS: A convenience sample of medical students, residents, and emergency physicians from a single academic emergency department were invited to participate. Participants included both novices and experts in POCUS. Each participant completed a baseline questionnaire and viewed 16 video clips of a FAST ultrasound examination while their gaze patterns were recorded by a commercially available eye-tracking device. The primary outcome was total gaze time on the area of interest (AOI). Secondary outcomes included total time to fixation, mean number of fixations, and mean duration of first fixation on the AOI. RESULTS: Fifteen novices and 15 experts completed this study. For total gaze time on the AOI, experts fixated their gaze significantly longer than novices (75.8 ± 16.2 seconds vs. 56.6 ± 12.8 seconds, p = 0.001). Similarly, experts were significantly faster to fixate on the AOI and had a higher fixation count on the AOI (8.5 ± 4.0 seconds vs. 15.1 ± 6.8 seconds, p = 0.003; and 170 ± 30 vs. 143 ± 28 seconds, p = 0.016). There were no differences on the mean duration of first fixation on the AOI (0.42 ± 0.12 seconds vs. 0.39 ± 0.09 seconds, p = 0.467). CONCLUSION: Eye-tracking technology shows the potential to differentiate between experts and novices by their gaze patterns on video clips of FAST examinations. The total gaze time on the AOI may be a useful metric to help in the assessment of competency in POCUS image interpretation. In addition, the evaluation of gaze patterns may help educators identify causes of interpretation errors. Future studies are needed to further validate these metrics in a larger cohort.
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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 | 0.005 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".