Replacing experts with student raters for assessing knee recess distension in ultrasound images of adult patients
Bibliographic record
Abstract
This study investigates the feasibility of replacing expert ratings with those from trained students in medical imaging research, specifically focusing on adult knee recess distension in ultrasound images. Expert radiologists provide high precision, but their limited availability and high cost can restrict scalability for large datasets. Ten university students with limited prior experience received structured training from a professional sonographer. The training included foundational knowledge of knee recess distension, segmentation practice using Label Studio software, and guidance on submission protocols. Students segmented ultrasound images from 25 patients diagnosed with recess distension, with each student performing three segmentations per image. An expert radiologist segmented the same images to establish a benchmark. Metrics such as Coefficient of Variation (CV), Intraclass Correlation Coefficient (ICC), and Root Mean Square Error (RMSE) were used to assess the accuracy and consistency of the students’ ratings compared to the expert. A simulation further evaluated the impact of variability and aggregation size on accuracy. Most students achieved ICC values above 0.80, indicating good to excellent agreement with the expert. Some students showed moderate agreement (ICC = 0.76) and higher RMSE values, reflecting variability in performance. The simulation revealed that aggregation reduces RMSE, though it eventually reaches a saturation point. For low-variability student ratings (CV = 0.55), accuracy comparable to expert benchmarks was achievable with smaller groups. Higher variability required larger groups, and in some cases, the lowest expert CV benchmarks (CV = 0.2) were unattainable. Trained student raters have the potential to serve as cost-effective alternatives to expert radiologists in large-scale imaging studies. By optimizing training and leveraging aggregation, student raters can achieve accuracy approaching expert levels, offering a scalable solution for resource-limited settings in medical imaging research.
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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 | 0.050 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".