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Purpose <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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Replacing experts with student raters for assessing knee recess distension in ultrasound images of adult patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月11日

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  • R. Fallahpour,
  • L. H. Radigan,
  • M. Mendez,
  • M. Nashnoush,
  • Pascal. N. Tyrrell

摘要

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.