Assessing Surgical Skill in Orthopedic Trauma Surgery Training: Behavioral Metrics for Digital Performance Evaluation
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ID: 315809
2026
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Abstract
Abstract Background Surgical skill assessment in orthopedic trauma surgery still relies on subjective expert ratings, limiting consistency and scalability. Although digitalization offers opportunities for objective evaluation, the manual and haptic nature of surgery makes digital capture of tool use challenging, keeping such approaches underdeveloped. Aims This study introduces a digital framework that uses optical tracking to create a digital twin of surgical real-world procedures with realistic haptics, enabling extraction of digital behavioral metrics (DBM). It investigates (1) which DBM reflect technical proficiency and (2) how well these metrics predict surgical performance compared to expert assessment. Methods 28 participants performed three standardized fracture fixations on synthetic bone models of the radius, ulna, and fibula. Tool motion was captured and transformed into a digital twin from which metrics such as path length, smoothness, and task duration were derived. (Figure 1) These metrics were statistically compared to benchmark performance scores, defined as the average of four expert ratings using the Global Rating Scale (GRS). (1) Correlation analysis identified skill-relevant metrics, and (2) a predictive model was trained to estimate performance from DBM evaluating its accuracy against the expert ratings. Results (1) Several DBM were found to be indicative of surgical performance. Measures based on tool path length and time per activity showed strong correlations with expert ratings, reaching coefficients of up to 0.6. Correlation strength varied across tools and procedures. (Figure 2) (2) The predictive model achieved a mean absolute difference from the benchmark score of 3.8 on the GRS scale (range: 28–70 points), outperforming the average inter-expert difference of 4.6 points. (Figure 3) Conclusion DBM were identified as valid indicators of surgical skill. Their predictive performance exceeded the agreement between individual experts, demonstrating the potential for objective, expert-independent assessment using digital performance evaluation frameworks.Figure 1:Training setup with synthetic bone models, surgical tools, and implants. Motion tracking via reflective markers creates a real-time digital twin for objective performance assessment.For image description, please refer to the figure legend and surrounding text. Figure 2:Spearman correlation coefficients between average GRS scores and DBM, stratified by tool and procedure type (radius, malleolus, ulna). Each bar shows the strength and direction of the correlation. Higher absolute values indicate stronger associa.For image description, please refer to the figure legend and surrounding text. Figure 3:The average GRS score is plotted against the GRS scores predicted by the linear mixed-effects model using leave-one-out cross-validation (blue dots). Individual expert ratings are shown in light gray for reference.For image description, please refer to the figure legend and surrounding text.
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openalex_W7163380717
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| Authors | T Schlegel, T Stauffer, N Diverse, F Tillmann, F Beeres, Q Lohmeyer, R Babst, M Meboldt |
| Journal | the british journal of surgery |
| Year | 2026 |
| DOI |
10.1093/bjs/znag055.061
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| URL | |
| Keywords | Keywords not found |
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