Measuring hand movement for suturing skill assessment: A simulation-based study

Background: To maximize patient safety, surgical skills education is increasingly adopting simulation -based curricula for formative skills assessment and training. However, many standardized assessment tools rely on human raters for performance assessment, which is resource-intensive and subjective. Simulators that provide automated and objective metrics from sensor data can address this limitation. We present an instrumented bench suturing simulator, patterned after the clock face radial suturing model from the Fundamentals of Vascular Surgery, for automated and objective assessment of open suturing skills.Methods: For this study, 97 participants (35 attending surgeons, 32 residents, and 30 novices) were recruited at national vascular conferences. Automated hand motion metrics, especially focusing on rota-tional motion analysis, were developed from the inertial measurement unit attached to participants' hands, and the proposed suite of metrics was used to differentiate between the skill levels of the 3 groups. Results: Attendings' and residents' performances were found to be significantly different from novices for all metrics. Moreover, most of our novel metrics could successfully distinguish between finer skill differences between attending and resident groups. In contrast, traditional operative skill metrics, such as time and path length, were unable to distinguish attendings from residents.Conclusion: This study provides evidence for the effectiveness of rotational motion analysis in assessing suturing skills. The suite of inertial measurement unit-based hand motion metrics introduced in this study allows for the incorporation of hand movement data for suturing skill assessment.(c) 2023 Elsevier Inc. All rights reserved.